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2308.02665
Gabriel Roccabruna
Michele Yin, Gabriel Roccabruna, Abhinav Azad, Giuseppe Riccardi
Let's Give a Voice to Conversational Agents in Virtual Reality
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The dialogue experience with conversational agents can be greatly enhanced with multimodal and immersive interactions in virtual reality. In this work, we present an open-source architecture with the goal of simplifying the development of conversational agents operating in virtual environments. The architecture offers the possibility of plugging in conversational agents of different domains and adding custom or cloud-based Speech-To-Text and Text-To-Speech models to make the interaction voice-based. Using this architecture, we present two conversational prototypes operating in the digital health domain developed in Unity for both non-immersive displays and VR headsets.
[ { "version": "v1", "created": "Fri, 4 Aug 2023 18:51:38 GMT" } ]
1,691,452,800,000
[ [ "Yin", "Michele", "" ], [ "Roccabruna", "Gabriel", "" ], [ "Azad", "Abhinav", "" ], [ "Riccardi", "Giuseppe", "" ] ]
2308.02666
Justin Stevens
Justin Stevens, Vadim Bulitko, David Thue
Solving Witness-type Triangle Puzzles Faster with an Automatically Learned Human-Explainable Predicate
10 pages
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Automatically solving puzzle instances in the game The Witness can guide players toward solutions and help puzzle designers generate better puzzles. In the latter case such an Artificial Intelligence puzzle solver can inform a human puzzle designer and procedural puzzle generator to produce better instances. The puzzles, however, are combinatorially difficult and search-based solvers can require large amounts of time and memory. We accelerate such search by automatically learning a human-explainable predicate that predicts whether a partial path to a Witness-type puzzle is not completable to a solution path. We prove a key property of the learned predicate which allows us to use it for pruning successor states in search thereby accelerating search by an average of six times while maintaining completeness of the underlying search. Conversely given a fixed search time budget per puzzle our predicate-accelerated search can solve more puzzle instances of larger sizes than the baseline search.
[ { "version": "v1", "created": "Fri, 4 Aug 2023 18:52:18 GMT" } ]
1,691,452,800,000
[ [ "Stevens", "Justin", "" ], [ "Bulitko", "Vadim", "" ], [ "Thue", "David", "" ] ]
2308.02730
Mucahit Cevik
Mucahit Cevik, Can Kavaklioglu, Fahad Razak, Amol Verma, Ayse Basar
Assessing the impact of emergency department short stay units using length-of-stay prediction and discrete event simulation
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Accurately predicting hospital length-of-stay at the time a patient is admitted to hospital may help guide clinical decision making and resource allocation. In this study we aim to build a decision support system that predicts hospital length-of-stay for patients admitted to general internal medicine from the emergency department. We conduct an exploratory data analysis and employ feature selection methods to identify the attributes that result in the best predictive performance. We also develop a discrete-event simulation model to assess the performances of the prediction models in a practical setting. Our results show that the recommendation performances of the proposed approaches are generally acceptable and do not benefit from the feature selection. Further, the results indicate that hospital length-of-stay could be predicted with reasonable accuracy (e.g., AUC value for classifying short and long stay patients is 0.69) using patient admission demographics, laboratory test results, diagnostic imaging, vital signs and clinical documentation.
[ { "version": "v1", "created": "Fri, 4 Aug 2023 22:26:02 GMT" } ]
1,691,452,800,000
[ [ "Cevik", "Mucahit", "" ], [ "Kavaklioglu", "Can", "" ], [ "Razak", "Fahad", "" ], [ "Verma", "Amol", "" ], [ "Basar", "Ayse", "" ] ]
2308.02835
Chathura Gamage
Chathura Gamage, Vimukthini Pinto, Matthew Stephenson, Jochen Renz
Physics-Based Task Generation through Causal Sequence of Physical Interactions
The 19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-23)
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.
[ { "version": "v1", "created": "Sat, 5 Aug 2023 10:15:18 GMT" }, { "version": "v2", "created": "Wed, 16 Aug 2023 16:51:45 GMT" } ]
1,692,230,400,000
[ [ "Gamage", "Chathura", "" ], [ "Pinto", "Vimukthini", "" ], [ "Stephenson", "Matthew", "" ], [ "Renz", "Jochen", "" ] ]
2308.02950
Louis Vervoort
Louis Vervoort, Vitaliy Mizyakov, Anastasia Ugleva
A criterion for Artificial General Intelligence: hypothetic-deductive reasoning, tested on ChatGPT
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We argue that a key reasoning skill that any advanced AI, say GPT-4, should master in order to qualify as 'thinking machine', or AGI, is hypothetic-deductive reasoning. Problem-solving or question-answering can quite generally be construed as involving two steps: hypothesizing that a certain set of hypotheses T applies to the problem or question at hand, and deducing the solution or answer from T - hence the term hypothetic-deductive reasoning. An elementary proxy of hypothetic-deductive reasoning is causal reasoning. We propose simple tests for both types of reasoning, and apply them to ChatGPT. Our study shows that, at present, the chatbot has a limited capacity for either type of reasoning, as soon as the problems considered are somewhat complex. However, we submit that if an AI would be capable of this type of reasoning in a sufficiently wide range of contexts, it would be an AGI.
[ { "version": "v1", "created": "Sat, 5 Aug 2023 20:33:13 GMT" } ]
1,691,452,800,000
[ [ "Vervoort", "Louis", "" ], [ "Mizyakov", "Vitaliy", "" ], [ "Ugleva", "Anastasia", "" ] ]
2308.03028
Lei Song
Lei Song, Chuheng Zhang, Li Zhao, Jiang Bian
Pre-Trained Large Language Models for Industrial Control
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained from pre-training with Internet-scale corpus, have the potential to be a good controller with proper prompts. In this paper, we take HVAC (Heating, Ventilation, and Air Conditioning) building control as an example to examine the ability of GPT-4 (one of the first-tier foundation models) as the controller. To control HVAC, we wrap the task as a language game by providing text including a short description for the task, several selected demonstrations, and the current observation to GPT-4 on each step and execute the actions responded by GPT-4. We conduct series of experiments to answer the following questions: 1)~How well can GPT-4 control HVAC? 2)~How well can GPT-4 generalize to different scenarios for HVAC control? 3) How different parts of the text context affect the performance? In general, we found GPT-4 achieves the performance comparable to RL methods with few samples and low technical debt, indicating the potential of directly applying foundation models to industrial control tasks.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 06:01:18 GMT" } ]
1,691,452,800,000
[ [ "Song", "Lei", "" ], [ "Zhang", "Chuheng", "" ], [ "Zhao", "Li", "" ], [ "Bian", "Jiang", "" ] ]
2308.03107
Ruoling Peng
Ruoling Peng, Kang Liu, Po Yang, Zhipeng Yuan, Shunbao Li
Embedding-based Retrieval with LLM for Effective Agriculture Information Extracting from Unstructured Data
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Pest identification is a crucial aspect of pest control in agriculture. However, most farmers are not capable of accurately identifying pests in the field, and there is a limited number of structured data sources available for rapid querying. In this work, we explored using domain-agnostic general pre-trained large language model(LLM) to extract structured data from agricultural documents with minimal or no human intervention. We propose a methodology that involves text retrieval and filtering using embedding-based retrieval, followed by LLM question-answering to automatically extract entities and attributes from the documents, and transform them into structured data. In comparison to existing methods, our approach achieves consistently better accuracy in the benchmark while maintaining efficiency.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 13:18:38 GMT" } ]
1,691,452,800,000
[ [ "Peng", "Ruoling", "" ], [ "Liu", "Kang", "" ], [ "Yang", "Po", "" ], [ "Yuan", "Zhipeng", "" ], [ "Li", "Shunbao", "" ] ]
2308.03150
Nemali Venkat Sai Abhishek
N V S Abhishek, Pushpak Bhattacharyya
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Speech Emotion Recognition (SER) is the task of identifying the emotion expressed in a spoken utterance. Emotion recognition is essential in building robust conversational agents in domains such as law, healthcare, education, and customer support. Most of the studies published on SER use datasets created by employing professional actors in a noise-free environment. In natural settings such as a customer care conversation, the audio is often noisy with speakers regularly switching between different languages as they see fit. We have worked in collaboration with a leading unicorn in the Conversational AI sector to develop Natural Speech Emotion Dataset (NSED). NSED is a natural code-mixed speech emotion dataset where each utterance in a conversation is annotated with emotion, sentiment, valence, arousal, and dominance (VAD) values. In this paper, we show that by incorporating word-level VAD value we improve on the task of SER by 2%, for negative emotions, over the baseline value for NSED. High accuracy for negative emotion recognition is essential because customers expressing negative opinions/views need to be pacified with urgency, lest complaints and dissatisfaction snowball and get out of hand. Escalation of negative opinions speedily is crucial for business interests. Our study then can be utilized to develop conversational agents which are more polite and empathetic in such situations.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 15:56:12 GMT" } ]
1,691,452,800,000
[ [ "Abhishek", "N V S", "" ], [ "Bhattacharyya", "Pushpak", "" ] ]
2308.03161
Rafa\"el Brandt
Rafa\"el Brandt, Daan Raatjens, Georgi Gaydadjiev
Precise Benchmarking of Explainable AI Attribution Methods
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The rationale behind a deep learning model's output is often difficult to understand by humans. EXplainable AI (XAI) aims at solving this by developing methods that improve interpretability and explainability of machine learning models. Reliable evaluation metrics are needed to assess and compare different XAI methods. We propose a novel evaluation approach for benchmarking state-of-the-art XAI attribution methods. Our proposal consists of a synthetic classification model accompanied by its derived ground truth explanations allowing high precision representation of input nodes contributions. We also propose new high-fidelity metrics to quantify the difference between explanations of the investigated XAI method and those derived from the synthetic model. Our metrics allow assessment of explanations in terms of precision and recall separately. Also, we propose metrics to independently evaluate negative or positive contributions of inputs. Our proposal provides deeper insights into XAI methods output. We investigate our proposal by constructing a synthetic convolutional image classification model and benchmarking several widely used XAI attribution methods using our evaluation approach. We compare our results with established prior XAI evaluation metrics. By deriving the ground truth directly from the constructed model in our method, we ensure the absence of bias, e.g., subjective either based on the training set. Our experimental results provide novel insights into the performance of Guided-Backprop and Smoothgrad XAI methods that are widely in use. Both have good precision and recall scores among positively contributing pixels (0.7, 0.76 and 0.7, 0.77, respectively), but poor precision scores among negatively contributing pixels (0.44, 0.61 and 0.47, 0.75, resp.). The recall scores in the latter case remain close. We show that our metrics are among the fastest in terms of execution time.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 17:03:32 GMT" } ]
1,691,452,800,000
[ [ "Brandt", "Rafaël", "" ], [ "Raatjens", "Daan", "" ], [ "Gaydadjiev", "Georgi", "" ] ]
2308.03176
Shuang Ao
Shuang Ao
Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language Processing
4 pages
2023
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for safety-critical tasks. One shared characteristic of these critical tasks is their risk sensitivity, where small mistakes can cause big consequences and even endanger life. There are several factors that could be guidelines for the successful deployment of AI systems in sensitive tasks: (i) failure detection and out-of-distribution (OOD) detection; (ii) overfitting identification; (iii) uncertainty quantification for predictions; (iv) robustness to data perturbations. These factors are also challenges of current AI systems, which are major blocks for building safe and reliable AI. Specifically, the current AI algorithms are unable to identify common causes for failure detection. Furthermore, additional techniques are required to quantify the quality of predictions. All these contribute to inaccurate uncertainty quantification, which lowers trust in predictions. Hence obtaining accurate model uncertainty quantification and its further improvement are challenging. To address these issues, many techniques have been proposed, such as regularization methods and learning strategies. As vision and language are the most typical data type and have many open source benchmark datasets, this thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering. In this thesis, we aim to build a safeguard by further developing current techniques to ensure the accurate model uncertainty for safety-critical tasks.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 18:05:59 GMT" } ]
1,691,452,800,000
[ [ "Ao", "Shuang", "" ] ]
2308.03179
Shuang Ao
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Empirical Optimal Risk to Quantify Model Trustworthiness for Failure Detection
7 pages
2023
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Failure detection (FD) in AI systems is a crucial safeguard for the deployment for safety-critical tasks. The common evaluation method of FD performance is the Risk-coverage (RC) curve, which reveals the trade-off between the data coverage rate and the performance on accepted data. One common way to quantify the RC curve by calculating the area under the RC curve. However, this metric does not inform on how suited any method is for FD, or what the optimal coverage rate should be. As FD aims to achieve higher performance with fewer data discarded, evaluating with partial coverage excluding the most uncertain samples is more intuitive and meaningful than full coverage. In addition, there is an optimal point in the coverage where the model could achieve ideal performance theoretically. We propose the Excess Area Under the Optimal RC Curve (E-AUoptRC), with the area in coverage from the optimal point to the full coverage. Further, the model performance at this optimal point can represent both model learning ability and calibration. We propose it as the Trust Index (TI), a complementary evaluation metric to the overall model accuracy. We report extensive experiments on three benchmark image datasets with ten variants of transformer and CNN models. Our results show that our proposed methods can better reflect the model trustworthiness than existing evaluation metrics. We further observe that the model with high overall accuracy does not always yield the high TI, which indicates the necessity of the proposed Trust Index as a complementary metric to the model overall accuracy. The code are available at \url{https://github.com/AoShuang92/optimal_risk}.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 18:11:42 GMT" } ]
1,691,452,800,000
[ [ "Ao", "Shuang", "" ], [ "Rueger", "Stefan", "" ], [ "Siddharthan", "Advaith", "" ] ]
2308.03185
Guangmo Tong
Mina Samizadeh, Guangmo Tong
VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs
CIKM 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Data-driven approaches have been proven effective in solving combinatorial optimization problems over graphs such as the traveling salesman problems and the vehicle routing problem. The rationale behind such methods is that the input instances may follow distributions with salient patterns that can be leveraged to overcome the worst-case computational hardness. For optimization problems over graphs, the common practice of neural combinatorial solvers consumes the inputs in the form of adjacency matrices. In this paper, we explore a vision-based method that is conceptually novel: can neural models solve graph optimization problems by \textit{taking a look at the graph pattern}? Our results suggest that the performance of such vision-based methods is not only non-trivial but also comparable to the state-of-the-art matrix-based methods, which opens a new avenue for developing data-driven optimization solvers.
[ { "version": "v1", "created": "Sun, 6 Aug 2023 18:33:11 GMT" } ]
1,691,452,800,000
[ [ "Samizadeh", "Mina", "" ], [ "Tong", "Guangmo", "" ] ]
2308.03358
Jingdi Chen
Jingdi Chen, Tian Lan, Carlee Joe-Wong
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Communication is crucial for solving cooperative Multi-Agent Reinforcement Learning tasks in partially observable Markov Decision Processes. Existing works often rely on black-box methods to encode local information/features into messages shared with other agents, leading to the generation of continuous messages with high communication overhead and poor interpretability. Prior attempts at discrete communication methods generate one-hot vectors trained as part of agents' actions and use the Gumbel softmax operation for calculating message gradients, which are all heuristic designs that do not provide any quantitative guarantees on the expected return. This paper establishes an upper bound on the return gap between an ideal policy with full observability and an optimal partially observable policy with discrete communication. This result enables us to recast multi-agent communication into a novel online clustering problem over the local observations at each agent, with messages as cluster labels and the upper bound on the return gap as clustering loss. To minimize the return gap, we propose the Return-Gap-Minimization Communication (RGMComm) algorithm, which is a surprisingly simple design of discrete message generation functions and is integrated with reinforcement learning through the utilization of a novel Regularized Information Maximization loss function, which incorporates cosine-distance as the clustering metric. Evaluations show that RGMComm significantly outperforms state-of-the-art multi-agent communication baselines and can achieve nearly optimal returns with few-bit messages that are naturally interpretable.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 07:26:55 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 14:06:59 GMT" }, { "version": "v3", "created": "Tue, 29 Aug 2023 19:25:33 GMT" }, { "version": "v4", "created": "Wed, 13 Dec 2023 19:40:40 GMT" }, { "version": "v5", "created": "Mon, 18 Dec 2023 20:20:19 GMT" } ]
1,703,030,400,000
[ [ "Chen", "Jingdi", "" ], [ "Lan", "Tian", "" ], [ "Joe-Wong", "Carlee", "" ] ]
2308.03376
Olivier Spanjaard
Hugo Gilbert (LAMSADE), Mohamed Ouaguenouni, Meltem Ozturk (LAMSADE), Olivier Spanjaard
Robust Ordinal Regression for Subsets Comparisons with Interactions
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper is dedicated to a robust ordinal method for learning the preferences of a decision maker between subsets. The decision model, derived from Fishburn and LaValle (1996) and whose parameters we learn, is general enough to be compatible with any strict weak order on subsets, thanks to the consideration of possible interactions between elements. Moreover, we accept not to predict some preferences if the available preference data are not compatible with a reliable prediction. A predicted preference is considered reliable if all the simplest models (Occam's razor) explaining the preference data agree on it. Following the robust ordinal regression methodology, our predictions are based on an uncertainty set encompassing the possible values of the model parameters. We define a robust ordinal dominance relation between subsets and we design a procedure to determine whether this dominance relation holds. Numerical tests are provided on synthetic and real-world data to evaluate the richness and reliability of the preference predictions made.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 07:54:33 GMT" } ]
1,691,452,800,000
[ [ "Gilbert", "Hugo", "", "LAMSADE" ], [ "Ouaguenouni", "Mohamed", "", "LAMSADE" ], [ "Ozturk", "Meltem", "", "LAMSADE" ], [ "Spanjaard", "Olivier", "" ] ]
2308.03377
Moyu Zhang
Moyu Zhang, Xinning Zhu, Chunhong Zhang, Wenchen Qian, Feng Pan, Hui Zhao
Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts
Accepted by CIKM 2023, 10 pages, 5 figures, 4 tables
CIKM 2023
10.1145/3583780.3614827
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery. However, purely predicting student responses without imposing specific constraints on hidden concept mastery values does not guarantee the accuracy of these intermediate values as concept mastery values. To address this issue, we propose a principled approach called Counterfactual Monotonic Knowledge Tracing (CMKT), which builds on the implicit paradigm described above by using a counterfactual assumption to constrain the evolution of students' mastery of knowledge concepts.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 07:57:26 GMT" } ]
1,693,958,400,000
[ [ "Zhang", "Moyu", "" ], [ "Zhu", "Xinning", "" ], [ "Zhang", "Chunhong", "" ], [ "Qian", "Wenchen", "" ], [ "Pan", "Feng", "" ], [ "Zhao", "Hui", "" ] ]
2308.03427
Jingqing Ruan
Jingqing Ruan, Yihong Chen, Bin Zhang, Zhiwei Xu, Tianpeng Bao, Guoqing Du, Shiwei Shi, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao
TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
Accepted in NeurIPS-2023 Workshop on Foundation Models for Decision Making
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With recent advancements in natural language processing, Large Language Models (LLMs) have emerged as powerful tools for various real-world applications. Despite their prowess, the intrinsic generative abilities of LLMs may prove insufficient for handling complex tasks which necessitate a combination of task planning and the usage of external tools. In this paper, we first propose a structured framework tailored for LLM-based AI Agents and discuss the crucial capabilities necessary for tackling intricate problems. Within this framework, we design two distinct types of agents (i.e., one-step agent and sequential agent) to execute the inference process. Subsequently, we instantiate the framework using various LLMs and evaluate their Task Planning and Tool Usage (TPTU) abilities on typical tasks. By highlighting key findings and challenges, our goal is to provide a helpful resource for researchers and practitioners to leverage the power of LLMs in their AI applications. Our study emphasizes the substantial potential of these models, while also identifying areas that need more investigation and improvement.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 09:22:03 GMT" }, { "version": "v2", "created": "Sun, 22 Oct 2023 10:53:54 GMT" }, { "version": "v3", "created": "Tue, 7 Nov 2023 11:15:11 GMT" } ]
1,699,401,600,000
[ [ "Ruan", "Jingqing", "" ], [ "Chen", "Yihong", "" ], [ "Zhang", "Bin", "" ], [ "Xu", "Zhiwei", "" ], [ "Bao", "Tianpeng", "" ], [ "Du", "Guoqing", "" ], [ "Shi", "Shiwei", "" ], [ "Mao", "Hangyu", "" ], [ "Li", "Ziyue", "" ], [ "Zeng", "Xingyu", "" ], [ "Zhao", "Rui", "" ] ]
2308.03447
Rita T. Sousa
Rita T. Sousa, Sara Silva, Heiko Paulheim, Catia Pesquita
Biomedical Knowledge Graph Embeddings with Negative Statements
19 pages, 4 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under an Open World Assumption. Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction. However, no attention has been given to the exploration of negative statements by knowledge graph embedding approaches despite the potential of negative statements to produce more accurate representations of entities in a knowledge graph. We propose a novel approach, TrueWalks, to incorporate negative statements into the knowledge graph representation learning process. In particular, we present a novel walk-generation method that is able to not only differentiate between positive and negative statements but also take into account the semantic implications of negation in ontology-rich knowledge graphs. This is of particular importance for applications in the biomedical domain, where the inadequacy of embedding approaches regarding negative statements at the ontology level has been identified as a crucial limitation. We evaluate TrueWalks in ontology-rich biomedical knowledge graphs in two different predictive tasks based on KG embeddings: protein-protein interaction prediction and gene-disease association prediction. We conduct an extensive analysis over established benchmarks and demonstrate that our method is able to improve the performance of knowledge graph embeddings on all tasks.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 10:08:25 GMT" } ]
1,691,452,800,000
[ [ "Sousa", "Rita T.", "" ], [ "Silva", "Sara", "" ], [ "Paulheim", "Heiko", "" ], [ "Pesquita", "Catia", "" ] ]
2308.03450
Zicong Hong
Zicong Hong, Xiaoyu Qiu, Jian Lin, Wuhui Chen, Yue Yu, Hui Wang, Song Guo, Wen Gao
Intelligence-Endogenous Management Platform for Computing and Network Convergence
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Massive emerging applications are driving demand for the ubiquitous deployment of computing power today. This trend not only spurs the recent popularity of the \emph{Computing and Network Convergence} (CNC), but also introduces an urgent need for the intelligentization of a management platform to coordinate changing resources and tasks in the CNC. Therefore, in this article, we present the concept of an intelligence-endogenous management platform for CNCs called \emph{CNC brain} based on artificial intelligence technologies. It aims at efficiently and automatically matching the supply and demand with high heterogeneity in a CNC via four key building blocks, i.e., perception, scheduling, adaptation, and governance, throughout the CNC's life cycle. Their functionalities, goals, and challenges are presented. To examine the effectiveness of the proposed concept and framework, we also implement a prototype for the CNC brain based on a deep reinforcement learning technology. Also, it is evaluated on a CNC testbed that integrates two open-source and popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset provided by Microsoft Azure. The evaluation results prove the proposed method's effectiveness in terms of resource utilization and performance. Finally, we highlight the future research directions of the CNC brain.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 10:12:15 GMT" } ]
1,691,452,800,000
[ [ "Hong", "Zicong", "" ], [ "Qiu", "Xiaoyu", "" ], [ "Lin", "Jian", "" ], [ "Chen", "Wuhui", "" ], [ "Yu", "Yue", "" ], [ "Wang", "Hui", "" ], [ "Guo", "Song", "" ], [ "Gao", "Wen", "" ] ]
2308.03488
Moyu Zhang
Moyu Zhang, Xinning Zhu, Chunhong Zhang, Feng Pan, Wenchen Qian, Hui Zhao
No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
Accepted by CIKM 2023, 10 pages, 8 figures, 5 tables
CIKM 2023
10.1145/3583780.3614988
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of excessive or insufficient lengths. As sequences get longer, computational costs will increase exponentially. Therefore, KT methods usually truncate sequences to an acceptable length, which makes it difficult for models on online service systems to capture complete historical practice behaviors of students with too long sequences. Conversely, modeling students with short practice sequences using most KT methods may result in overfitting due to limited observation samples. To address the above limitations, we propose a model called Sequence-Flexible Knowledge Tracing (SFKT).
[ { "version": "v1", "created": "Mon, 7 Aug 2023 11:30:58 GMT" } ]
1,693,958,400,000
[ [ "Zhang", "Moyu", "" ], [ "Zhu", "Xinning", "" ], [ "Zhang", "Chunhong", "" ], [ "Pan", "Feng", "" ], [ "Qian", "Wenchen", "" ], [ "Zhao", "Hui", "" ] ]
2308.03527
Kristina Schaaff
Kristina Schaaff, Caroline Reinig, Tim Schlippe
Exploring ChatGPT's Empathic Abilities
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Empathy is often understood as the ability to share and understand another individual's state of mind or emotion. With the increasing use of chatbots in various domains, e.g., children seeking help with homework, individuals looking for medical advice, and people using the chatbot as a daily source of everyday companionship, the importance of empathy in human-computer interaction has become more apparent. Therefore, our study investigates the extent to which ChatGPT based on GPT-3.5 can exhibit empathetic responses and emotional expressions. We analyzed the following three aspects: (1) understanding and expressing emotions, (2) parallel emotional response, and (3) empathic personality. Thus, we not only evaluate ChatGPT on various empathy aspects and compare it with human behavior but also show a possible way to analyze the empathy of chatbots in general. Our results show, that in 91.7% of the cases, ChatGPT was able to correctly identify emotions and produces appropriate answers. In conversations, ChatGPT reacted with a parallel emotion in 70.7% of cases. The empathic capabilities of ChatGPT were evaluated using a set of five questionnaires covering different aspects of empathy. Even though the results show, that the scores of ChatGPT are still worse than the average of healthy humans, it scores better than people who have been diagnosed with Asperger syndrome / high-functioning autism.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 12:23:07 GMT" }, { "version": "v2", "created": "Thu, 14 Sep 2023 07:11:47 GMT" }, { "version": "v3", "created": "Fri, 22 Sep 2023 21:00:23 GMT" } ]
1,695,686,400,000
[ [ "Schaaff", "Kristina", "" ], [ "Reinig", "Caroline", "" ], [ "Schlippe", "Tim", "" ] ]
2308.03598
Mla{\dj}an Jovanovi\'c Dr
Peter Voss and Mladjan Jovanovic
Why We Don't Have AGI Yet
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The original vision of AI was re-articulated in 2002 via the term 'Artificial General Intelligence' or AGI. This vision is to build 'Thinking Machines' - computer systems that can learn, reason, and solve problems similar to the way humans do. This is in stark contrast to the 'Narrow AI' approach practiced by almost everyone in the field over the many decades. While several large-scale efforts have nominally been working on AGI (most notably DeepMind), the field of pure focused AGI development has not been well funded or promoted. This is surprising given the fantastic value that true AGI can bestow on humanity. In addition to the dearth of effort in this field, there are also several theoretical and methodical missteps that are hampering progress. We highlight why purely statistical approaches are unlikely to lead to AGI, and identify several crucial cognitive abilities required to achieve human-like adaptability and autonomous learning. We conclude with a survey of socio-technical factors that have undoubtedly slowed progress towards AGI.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 13:59:31 GMT" }, { "version": "v2", "created": "Sat, 19 Aug 2023 14:49:24 GMT" }, { "version": "v3", "created": "Wed, 30 Aug 2023 11:30:22 GMT" }, { "version": "v4", "created": "Tue, 19 Sep 2023 12:43:54 GMT" } ]
1,695,168,000,000
[ [ "Voss", "Peter", "" ], [ "Jovanovic", "Mladjan", "" ] ]
2308.03880
Juanita Puentes
Juanita Puentes, Angela Castillo, Wilmar Osejo, Yuly Calder\'on, Viviana Quintero, Lina Saldarriaga, Diana Agudelo and Pablo Arbel\'aez
Guarding the Guardians: Automated Analysis of Online Child Sexual Abuse
Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Recovery (HADR) workshop, ICCV 2023 in Paris, France
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Online violence against children has increased globally recently, demanding urgent attention. Competent authorities manually analyze abuse complaints to comprehend crime dynamics and identify patterns. However, the manual analysis of these complaints presents a challenge because it exposes analysts to harmful content during the review process. Given these challenges, we present a novel solution, an automated tool designed to analyze children's sexual abuse reports comprehensively. By automating the analysis process, our tool significantly reduces the risk of exposure to harmful content by categorizing the reports on three dimensions: Subject, Degree of Criminality, and Damage. Furthermore, leveraging our multidisciplinary team's expertise, we introduce a novel approach to annotate the collected data, enabling a more in-depth analysis of the reports. This approach improves the comprehension of fundamental patterns and trends, enabling law enforcement agencies and policymakers to create focused strategies in the fight against children's violence.
[ { "version": "v1", "created": "Mon, 7 Aug 2023 19:19:02 GMT" }, { "version": "v2", "created": "Thu, 10 Aug 2023 17:37:27 GMT" } ]
1,691,712,000,000
[ [ "Puentes", "Juanita", "" ], [ "Castillo", "Angela", "" ], [ "Osejo", "Wilmar", "" ], [ "Calderón", "Yuly", "" ], [ "Quintero", "Viviana", "" ], [ "Saldarriaga", "Lina", "" ], [ "Agudelo", "Diana", "" ], [ "Arbeláez", "Pablo", "" ] ]
2308.03992
Chen Cao
Cassie Chen Cao, Zijian Ding, Jionghao Lin, Frank Hopfgartner
AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
This study investigates the use of Artificial Intelligence (AI)-powered, multi-role chatbots as a means to enhance learning experiences and foster engagement in computer science education. Leveraging a design-based research approach, we develop, implement, and evaluate a novel learning environment enriched with four distinct chatbot roles: Instructor Bot, Peer Bot, Career Advising Bot, and Emotional Supporter Bot. These roles, designed around the tenets of Self-Determination Theory, cater to the three innate psychological needs of learners - competence, autonomy, and relatedness. Additionally, the system embraces an inquiry-based learning paradigm, encouraging students to ask questions, seek solutions, and explore their curiosities. We test this system in a higher education context over a period of one month with 200 participating students, comparing outcomes with conditions involving a human tutor and a single chatbot. Our research utilizes a mixed-methods approach, encompassing quantitative measures such as chat log sequence analysis, and qualitative methods including surveys and focus group interviews. By integrating cutting-edge Natural Language Processing techniques such as topic modelling and sentiment analysis, we offer an in-depth understanding of the system's impact on learner engagement, motivation, and inquiry-based learning. This study, through its rigorous design and innovative approach, provides significant insights into the potential of AI-empowered, multi-role chatbots in reshaping the landscape of computer science education and fostering an engaging, supportive, and motivating learning environment.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 02:13:44 GMT" } ]
1,691,539,200,000
[ [ "Cao", "Cassie Chen", "" ], [ "Ding", "Zijian", "" ], [ "Lin", "Jionghao", "" ], [ "Hopfgartner", "Frank", "" ] ]
2308.04026
Jiaju Lin
Jiaju Lin, Haoran Zhao, Aochi Zhang, Yiting Wu, Huqiuyue Ping, Qin Chen
AgentSims: An Open-Source Sandbox for Large Language Model Evaluation
submit to EMNLP2023 demo track
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
With ChatGPT-like large language models (LLM) prevailing in the community, how to evaluate the ability of LLMs is an open question. Existing evaluation methods suffer from following shortcomings: (1) constrained evaluation abilities, (2) vulnerable benchmarks, (3) unobjective metrics. We suggest that task-based evaluation, where LLM agents complete tasks in a simulated environment, is a one-for-all solution to solve above problems. We present AgentSims, an easy-to-use infrastructure for researchers from all disciplines to test the specific capacities they are interested in. Researchers can build their evaluation tasks by adding agents and buildings on an interactive GUI or deploy and test new support mechanisms, i.e. memory, planning and tool-use systems, by a few lines of codes. Our demo is available at https://agentsims.com .
[ { "version": "v1", "created": "Tue, 8 Aug 2023 03:59:28 GMT" } ]
1,691,539,200,000
[ [ "Lin", "Jiaju", "" ], [ "Zhao", "Haoran", "" ], [ "Zhang", "Aochi", "" ], [ "Wu", "Yiting", "" ], [ "Ping", "Huqiuyue", "" ], [ "Chen", "Qin", "" ] ]
2308.04030
Zhiyuan Peng
Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Augmented Language Models (ALMs) empower large language models with the ability to use tools, transforming them into intelligent agents for real-world interactions. However, most existing frameworks for ALMs, to varying degrees, are deficient in the following critical features: flexible customization, collaborative democratization, and holistic evaluation. We present gentopia, an ALM framework enabling flexible customization of agents through simple configurations, seamlessly integrating various language models, task formats, prompting modules, and plugins into a unified paradigm. Furthermore, we establish gentpool, a public platform enabling the registration and sharing of user-customized agents. Agents registered in gentpool are composable such that they can be assembled together for agent collaboration, advancing the democratization of artificial intelligence. To ensure high-quality agents, gentbench, an integral component of gentpool, is designed to thoroughly evaluate user-customized agents across diverse aspects such as safety, robustness, efficiency, etc. We release gentopia on Github and will continuously move forward.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 04:12:29 GMT" } ]
1,691,539,200,000
[ [ "Xu", "Binfeng", "" ], [ "Liu", "Xukun", "" ], [ "Shen", "Hua", "" ], [ "Han", "Zeyu", "" ], [ "Li", "Yuhan", "" ], [ "Yue", "Murong", "" ], [ "Peng", "Zhiyuan", "" ], [ "Liu", "Yuchen", "" ], [ "Yao", "Ziyu", "" ], [ "Xu", "Dongkuan", "" ] ]
2308.04161
Frank Wolter
James P. Delgrande, Birte Glimm, Thomas Meyer, Miroslaw Truszczynski, Frank Wolter
Current and Future Challenges in Knowledge Representation and Reasoning
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 09:47:44 GMT" } ]
1,691,539,200,000
[ [ "Delgrande", "James P.", "" ], [ "Glimm", "Birte", "" ], [ "Meyer", "Thomas", "" ], [ "Truszczynski", "Miroslaw", "" ], [ "Wolter", "Frank", "" ] ]
2308.04172
Charlie Abela Dr
Lizzy Farrugia, Lilian M. Azzopardi, Jeremy Debattista and Charlie Abela
Predicting Drug-Drug Interactions Using Knowledge Graphs
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
In the last decades, people have been consuming and combining more drugs than before, increasing the number of Drug-Drug Interactions (DDIs). To predict unknown DDIs, recently, studies started incorporating Knowledge Graphs (KGs) since they are able to capture the relationships among entities providing better drug representations than using a single drug property. In this paper, we propose the medicX end-to-end framework that integrates several drug features from public drug repositories into a KG and embeds the nodes in the graph using various translation, factorisation and Neural Network (NN) based KG Embedding (KGE) methods. Ultimately, we use a Machine Learning (ML) algorithm that predicts unknown DDIs. Among the different translation and factorisation-based KGE models, we found that the best performing combination was the ComplEx embedding method with a Long Short-Term Memory (LSTM) network, which obtained an F1-score of 95.19% on a dataset based on the DDIs found in DrugBank version 5.1.8. This score is 5.61% better than the state-of-the-art model DeepDDI. Additionally, we also developed a graph auto-encoder model that uses a Graph Neural Network (GNN), which achieved an F1-score of 91.94%. Consequently, GNNs have demonstrated a stronger ability to mine the underlying semantics of the KG than the ComplEx model, and thus using higher dimension embeddings within the GNN can lead to state-of-the-art performance.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 10:07:22 GMT" }, { "version": "v2", "created": "Fri, 11 Aug 2023 07:54:24 GMT" } ]
1,691,971,200,000
[ [ "Farrugia", "Lizzy", "" ], [ "Azzopardi", "Lilian M.", "" ], [ "Debattista", "Jeremy", "" ], [ "Abela", "Charlie", "" ] ]
2308.04187
Lutz Terfloth
Lutz Terfloth, Michael Schaffer, Heike M. Buhl, Carsten Schulte
Adding Why to What? Analyses of an Everyday Explanation
Paper accepted and presented at XAI World Conference 2023, Lisboa
null
10.1007/978-3-031-44070-0_13
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In XAI it is important to consider that, in contrast to explanations for professional audiences, one cannot assume common expertise when explaining for laypeople. But such explanations between humans vary greatly, making it difficult to research commonalities across explanations. We used the dual nature theory, a techno-philosophical approach, to cope with these challenges. According to it, one can explain, for example, an XAI's decision by addressing its dual nature: by focusing on the Architecture (e.g., the logic of its algorithms) or the Relevance (e.g., the severity of a decision, the implications of a recommendation). We investigated 20 game explanations using the theory as an analytical framework. We elaborate how we used the theory to quickly structure and compare explanations of technological artifacts. We supplemented results from analyzing the explanation contents with results from a video recall to explore how explainers justified their explanation. We found that explainers were focusing on the physical aspects of the game first (Architecture) and only later on aspects of the Relevance. Reasoning in the video recalls indicated that EX regarded the focus on the Architecture as important for structuring the explanation initially by explaining the basic components before focusing on more complex, intangible aspects. Shifting between addressing the two sides was justified by explanation goals, emerging misunderstandings, and the knowledge needs of the explainee. We discovered several commonalities that inspire future research questions which, if further generalizable, provide first ideas for the construction of synthetic explanations.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 11:17:22 GMT" } ]
1,698,105,600,000
[ [ "Terfloth", "Lutz", "" ], [ "Schaffer", "Michael", "" ], [ "Buhl", "Heike M.", "" ], [ "Schulte", "Carsten", "" ] ]
2308.04265
Ninareh Mehrabi
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta
FLIRT: Feedback Loop In-context Red Teaming
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Warning: this paper contains content that may be inappropriate or offensive. As generative models become available for public use in various applications, testing and analyzing vulnerabilities of these models has become a priority. Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation. Our framework uses in-context learning in a feedback loop to red team models and trigger them into unsafe content generation. We propose different in-context attack strategies to automatically learn effective and diverse adversarial prompts for text-to-image models. Our experiments demonstrate that compared to baseline approaches, our proposed strategy is significantly more effective in exposing vulnerabilities in Stable Diffusion (SD) model, even when the latter is enhanced with safety features. Furthermore, we demonstrate that the proposed framework is effective for red teaming text-to-text models, resulting in significantly higher toxic response generation rate compared to previously reported numbers.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 14:03:08 GMT" } ]
1,691,539,200,000
[ [ "Mehrabi", "Ninareh", "" ], [ "Goyal", "Palash", "" ], [ "Dupuy", "Christophe", "" ], [ "Hu", "Qian", "" ], [ "Ghosh", "Shalini", "" ], [ "Zemel", "Richard", "" ], [ "Chang", "Kai-Wei", "" ], [ "Galstyan", "Aram", "" ], [ "Gupta", "Rahul", "" ] ]
2308.04299
Jakub {\L}yskawa
Jakub {\L}yskawa, Pawe{\l} Wawrzy\'nski
Actor-Critic with variable time discretization via sustained actions
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Reinforcement learning (RL) methods work in discrete time. In order to apply RL to inherently continuous problems like robotic control, a specific time discretization needs to be defined. This is a choice between sparse time control, which may be easier to train, and finer time control, which may allow for better ultimate performance. In this work, we propose SusACER, an off-policy RL algorithm that combines the advantages of different time discretization settings. Initially, it operates with sparse time discretization and gradually switches to a fine one. We analyze the effects of the changing time discretization in robotic control environments: Ant, HalfCheetah, Hopper, and Walker2D. In all cases our proposed algorithm outperforms state of the art.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 14:45:00 GMT" } ]
1,691,539,200,000
[ [ "Łyskawa", "Jakub", "" ], [ "Wawrzyński", "Paweł", "" ] ]
2308.04371
Yifan Zhang
Yifan Zhang, Jingqin Yang, Yang Yuan, Andrew Chi-Chih Yao
Cumulative Reasoning with Large Language Models
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Despite the recent advancements in language models (LMs), their ability to solve complex problems remains limited. This paper introduces Cumulative Reasoning (CR), a novel approach that utilizes LMs cumulatively and iteratively, mirroring human thought processes for problem-solving. CR decomposes tasks into smaller, manageable components and leverages previous propositions for effective composition, significantly enhancing problem-solving capabilities. We demonstrate CR's superiority through several complex reasoning tasks: it outperforms existing methods in logical inference tasks with up to a 9.3% improvement, achieving 98.04% accuracy on the curated FOLIO wiki dataset. In the Game of 24, it achieves 98% accuracy, marking a 24% improvement over the prior state-of-the-art. Additionally, CR sets new state-of-the-art on the MATH dataset, achieving a 4.2% increase from previous methods and a 43% relative improvement in the most challenging problems. By extending CR to incorporate a code environment without external aids like retrieval or web browsing, we further harness the computational and logical reasoning capabilities of LMs, achieving a remarkable 72.2% accuracy on the MATH dataset and outperforming the PAL/PoT method by 38.8%. Our work not only sets new state-of-the-art but also paves the way toward more sophisticated AI reasoning methods. The code is available at https://github.com/iiis-ai/cumulative-reasoning.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 16:18:20 GMT" }, { "version": "v2", "created": "Wed, 9 Aug 2023 14:37:37 GMT" }, { "version": "v3", "created": "Thu, 10 Aug 2023 08:24:09 GMT" }, { "version": "v4", "created": "Fri, 25 Aug 2023 02:40:37 GMT" }, { "version": "v5", "created": "Sat, 2 Dec 2023 02:59:12 GMT" }, { "version": "v6", "created": "Tue, 2 Apr 2024 03:37:39 GMT" } ]
1,712,102,400,000
[ [ "Zhang", "Yifan", "" ], [ "Yang", "Jingqin", "" ], [ "Yuan", "Yang", "" ], [ "Yao", "Andrew Chi-Chih", "" ] ]
2308.04372
Anthony Hunter
Anthony Hunter
Some Options for Instantiation of Bipolar Argument Graphs with Deductive Arguments
15 pages
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Argument graphs provide an abstract representation of an argumentative situation. A bipolar argument graph is a directed graph where each node denotes an argument, and each arc denotes the influence of one argument on another. Here we assume that the influence is supporting, attacking, or ambiguous. In a bipolar argument graph, each argument is atomic and so it has no internal structure. Yet to better understand the nature of the individual arguments, and how they interact, it is important to consider their internal structure. To address this need, this paper presents a framework based on the use of logical arguments to instantiate bipolar argument graphs, and a set of possible constraints on instantiating arguments that take into account the internal structure of the arguments, and the types of relationship between arguments.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 16:22:27 GMT" } ]
1,691,539,200,000
[ [ "Hunter", "Anthony", "" ] ]
2308.04492
Sang Yun Kwon
Sang Yun Kwon, Gagan Bhatia, El Moatez Billah Nagoud, Muhammad Abdul-Mageed
ChatGPT for Arabic Grammatical Error Correction
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Recently, large language models (LLMs) fine-tuned to follow human instruction have exhibited significant capabilities in various English NLP tasks. However, their performance in grammatical error correction (GEC) tasks, particularly in non-English languages, remains significantly unexplored. In this paper, we delve into abilities of instruction fine-tuned LLMs in Arabic GEC, a task made complex due to Arabic's rich morphology. Our findings suggest that various prompting methods, coupled with (in-context) few-shot learning, demonstrate considerable effectiveness, with GPT-4 achieving up to $65.49$ F\textsubscript{1} score under expert prompting (approximately $5$ points higher than our established baseline). This highlights the potential of LLMs in low-resource settings, offering a viable approach for generating useful synthetic data for model training. Despite these positive results, we find that instruction fine-tuned models, regardless of their size, significantly underperform compared to fully fine-tuned models of significantly smaller sizes. This disparity highlights a substantial room for improvements for LLMs. Inspired by methods from low-resource machine translation, we also develop a method exploiting synthetic data that significantly outperforms previous models on two standard Arabic benchmarks. Our work sets new SoTA for Arabic GEC, with $72.19\%$ and $73.26$ F$_{1}$ on the 2014 and 2015 QALB datasets, respectively.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 18:00:39 GMT" } ]
1,691,625,600,000
[ [ "Kwon", "Sang Yun", "" ], [ "Bhatia", "Gagan", "" ], [ "Nagoud", "El Moatez Billah", "" ], [ "Abdul-Mageed", "Muhammad", "" ] ]
2308.04586
Mark Stefik
Mark Stefik and Robert Price
Bootstrapping Developmental AIs: From Simple Competences to Intelligent Human-Compatible AIs
112 pages, 28 figures, 4 tables
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Developmental AI creates embodied AIs that develop human-like abilities. The AIs start with innate competences and learn more by interacting with the world including people. Developmental AIs have been demonstrated, but their abilities so far do not surpass those of pre-toddler children. In contrast, mainstream approaches have led to impressive feats and commercially valuable AI systems. The approaches include deep learning and generative AI (e.g., large language models) and manually constructed symbolic modeling. However, manually constructed AIs tend to be brittle even in circumscribed domains. Generative AIs are helpful on average, but they can make strange mistakes and not notice them. Not learning from their experience in the world, they can lack common sense and social alignment. This position paper lays out prospects, gaps, and challenges for a bootstrapping approach to developmental AI that follows a bio-inspired trajectory. The approach creates experiential foundation models for human-compatible AIs. A virtuous multidisciplinary research cycle has led to developmental AIs with capabilities for multimodal perception, object recognition, and manipulation. Computational models for hierarchical planning, abstraction discovery, curiosity, and language acquisition exist but need to be adapted to an embodied learning approach. The remaining gaps include nonverbal communication, speech, reading, and writing. These competences enable people to acquire socially developed competences. Aspirationally, developmental AIs would learn, share what they learn, and collaborate to achieve high standards. They would learn to communicate, establish common ground, read critically, consider the provenance of information, test hypotheses, and collaborate. The approach would make the training of AIs more democratic.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 21:14:21 GMT" }, { "version": "v10", "created": "Wed, 25 Oct 2023 17:33:24 GMT" }, { "version": "v11", "created": "Tue, 31 Oct 2023 16:46:54 GMT" }, { "version": "v12", "created": "Sat, 11 Nov 2023 15:43:13 GMT" }, { "version": "v13", "created": "Sat, 16 Dec 2023 15:19:11 GMT" }, { "version": "v14", "created": "Thu, 28 Dec 2023 17:48:24 GMT" }, { "version": "v15", "created": "Thu, 4 Jan 2024 16:31:09 GMT" }, { "version": "v16", "created": "Mon, 15 Jan 2024 14:40:51 GMT" }, { "version": "v17", "created": "Fri, 19 Jan 2024 12:07:13 GMT" }, { "version": "v18", "created": "Sun, 28 Jan 2024 15:36:04 GMT" }, { "version": "v19", "created": "Mon, 12 Feb 2024 07:50:00 GMT" }, { "version": "v2", "created": "Fri, 11 Aug 2023 15:33:28 GMT" }, { "version": "v20", "created": "Tue, 19 Mar 2024 16:18:02 GMT" }, { "version": "v21", "created": "Thu, 4 Apr 2024 01:40:00 GMT" }, { "version": "v3", "created": "Thu, 17 Aug 2023 16:31:33 GMT" }, { "version": "v4", "created": "Wed, 23 Aug 2023 18:38:29 GMT" }, { "version": "v5", "created": "Tue, 29 Aug 2023 21:41:31 GMT" }, { "version": "v6", "created": "Thu, 7 Sep 2023 23:06:24 GMT" }, { "version": "v7", "created": "Wed, 13 Sep 2023 17:13:52 GMT" }, { "version": "v8", "created": "Thu, 21 Sep 2023 17:46:49 GMT" }, { "version": "v9", "created": "Wed, 4 Oct 2023 22:59:10 GMT" } ]
1,712,275,200,000
[ [ "Stefik", "Mark", "" ], [ "Price", "Robert", "" ] ]
2308.04639
Tianshu Yu
Zhang-Hua Fu, Sipeng Sun, Jintong Ren, Tianshu Yu, Haoyu Zhang, Yuanyuan Liu, Lingxiao Huang, Xiang Yan, Pinyan Lu
A Hierarchical Destroy and Repair Approach for Solving Very Large-Scale Travelling Salesman Problem
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
For prohibitively large-scale Travelling Salesman Problems (TSPs), existing algorithms face big challenges in terms of both computational efficiency and solution quality. To address this issue, we propose a hierarchical destroy-and-repair (HDR) approach, which attempts to improve an initial solution by applying a series of carefully designed destroy-and-repair operations. A key innovative concept is the hierarchical search framework, which recursively fixes partial edges and compresses the input instance into a small-scale TSP under some equivalence guarantee. This neat search framework is able to deliver highly competitive solutions within a reasonable time. Fair comparisons based on nineteen famous large-scale instances (with 10,000 to 10,000,000 cities) show that HDR is highly competitive against existing state-of-the-art TSP algorithms, in terms of both efficiency and solution quality. Notably, on two large instances with 3,162,278 and 10,000,000 cities, HDR breaks the world records (i.e., best-known results regardless of computation time), which were previously achieved by LKH and its variants, while HDR is completely independent of LKH. Finally, ablation studies are performed to certify the importance and validity of the hierarchical search framework.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 00:44:02 GMT" } ]
1,691,625,600,000
[ [ "Fu", "Zhang-Hua", "" ], [ "Sun", "Sipeng", "" ], [ "Ren", "Jintong", "" ], [ "Yu", "Tianshu", "" ], [ "Zhang", "Haoyu", "" ], [ "Liu", "Yuanyuan", "" ], [ "Huang", "Lingxiao", "" ], [ "Yan", "Xiang", "" ], [ "Lu", "Pinyan", "" ] ]
2308.04719
Yang Li
Yang Li and Kun Xiong and Yingping Zhang and Jiangcheng Zhu and Stephen Mcaleer and Wei Pan and Jun Wang and Zonghong Dai and Yaodong Yang
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
28 pages, accepted by Transactions on Machine Learning Research (TMLR)
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi. By analyzing over 10,000 records of human Xiangqi play, we highlight the existence of both transitive and non-transitive elements within the game's strategic structure. To address non-transitivity, we introduce the JiangJun algorithm, an innovative combination of Monte-Carlo Tree Search (MCTS) and Policy Space Response Oracles (PSRO) designed to approximate a Nash equilibrium. We evaluate the algorithm empirically using a WeChat mini program and achieve a Master level with a 99.41\% win rate against human players. The algorithm's effectiveness in overcoming non-transitivity is confirmed by a plethora of metrics, such as relative population performance and visualization results. Our project site is available at \url{https://sites.google.com/view/jiangjun-site/}.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 05:48:58 GMT" } ]
1,691,625,600,000
[ [ "Li", "Yang", "" ], [ "Xiong", "Kun", "" ], [ "Zhang", "Yingping", "" ], [ "Zhu", "Jiangcheng", "" ], [ "Mcaleer", "Stephen", "" ], [ "Pan", "Wei", "" ], [ "Wang", "Jun", "" ], [ "Dai", "Zonghong", "" ], [ "Yang", "Yaodong", "" ] ]
2308.04749
Bing Han
Bing Han, Feifei Zhao, Yi Zeng, Wenxuan Pan, Guobin Shen
Enhancing Efficient Continual Learning with Dynamic Structure Development of Spiking Neural Networks
null
IJCAI2023 Camera ready
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Children possess the ability to learn multiple cognitive tasks sequentially, which is a major challenge toward the long-term goal of artificial general intelligence. Existing continual learning frameworks are usually applicable to Deep Neural Networks (DNNs) and lack the exploration on more brain-inspired, energy-efficient Spiking Neural Networks (SNNs). Drawing on continual learning mechanisms during child growth and development, we propose Dynamic Structure Development of Spiking Neural Networks (DSD-SNN) for efficient and adaptive continual learning. When learning a sequence of tasks, the DSD-SNN dynamically assigns and grows new neurons to new tasks and prunes redundant neurons, thereby increasing memory capacity and reducing computational overhead. In addition, the overlapping shared structure helps to quickly leverage all acquired knowledge to new tasks, empowering a single network capable of supporting multiple incremental tasks (without the separate sub-network mask for each task). We validate the effectiveness of the proposed model on multiple class incremental learning and task incremental learning benchmarks. Extensive experiments demonstrated that our model could significantly improve performance, learning speed and memory capacity, and reduce computational overhead. Besides, our DSD-SNN model achieves comparable performance with the DNNs-based methods, and significantly outperforms the state-of-the-art (SOTA) performance for existing SNNs-based continual learning methods.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 07:36:40 GMT" } ]
1,691,625,600,000
[ [ "Han", "Bing", "" ], [ "Zhao", "Feifei", "" ], [ "Zeng", "Yi", "" ], [ "Pan", "Wenxuan", "" ], [ "Shen", "Guobin", "" ] ]
2308.04778
Yasser KHALAFAOUI
Yasser Khalafaoui (Alteca, ETIS - UMR 8051, CY), Nistor Grozavu (ETIS - UMR 8051, CY), Basarab Matei (LIPN), Laurent-Walter Goix
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization
null
2022 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 2022, Singapore, Singapore. pp.1386-1391
10.1109/SSCI51031.2022.10022129
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
By combining related objects, unsupervised machine learning techniques aim to reveal the underlying patterns in a data set. Non-negative Matrix Factorization (NMF) is a data mining technique that splits data matrices by imposing restrictions on the elements' non-negativity into two matrices: one representing the data partitions and the other to represent the cluster prototypes of the data set. This method has attracted a lot of attention and is used in a wide range of applications, including text mining, clustering, language modeling, music transcription, and neuroscience (gene separation). The interpretation of the generated matrices is made simpler by the absence of negative values. In this article, we propose a study on multi-modal clustering algorithms and present a novel method called multi-modal multi-view non-negative matrix factorization, in which we analyze the collaboration of several local NMF models. The experimental results show the value of the proposed approach, which was evaluated using a variety of data sets, and the obtained results are very promising compared to state of art methods.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 08:06:03 GMT" } ]
1,691,625,600,000
[ [ "Khalafaoui", "Yasser", "", "Alteca, ETIS - UMR 8051, CY" ], [ "Grozavu", "Nistor", "", "ETIS\n - UMR 8051, CY" ], [ "Matei", "Basarab", "", "LIPN" ], [ "Goix", "Laurent-Walter", "" ] ]
2308.04814
Gunjan Singh
Gunjan Singh, Sumit Bhatia, Raghava Mutharaju
Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges
This paper is a part of the book titled Compendium of Neuro-Symbolic Artificial Intelligence which can be found at the following link: https://www.iospress.com/ catalog/books/compendium-of-neurosymbolic-artificial-intelligence
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development. RDF is favored for its simplicity and flexibility, while OWL enables detailed domain knowledge representation. However, as ontologies grow larger and more expressive, reasoning complexity increases, and traditional reasoners struggle to perform efficiently. Despite optimization efforts, scalability remains an issue. Additionally, advancements in automated knowledge base construction have created large and expressive ontologies that are often noisy and inconsistent, posing further challenges for conventional reasoners. To address these challenges, researchers have explored neuro-symbolic approaches that combine neural networks' learning capabilities with symbolic systems' reasoning abilities. In this chapter,we provide an overview of the existing literature in the field of neuro-symbolic deductive reasoning supported by RDF(S), the description logics EL and ALC, and OWL 2 RL, discussing the techniques employed, the tasks they address, and other relevant efforts in this area.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 09:12:35 GMT" } ]
1,691,625,600,000
[ [ "Singh", "Gunjan", "" ], [ "Bhatia", "Sumit", "" ], [ "Mutharaju", "Raghava", "" ] ]
2308.04914
Jiawen Kang
Xumin Huang, Yuan Wu, Jiawen Kang, Jiangtian Nie, Weifeng Zhong, Dong In Kim, and Shengli Xie
Service Reservation and Pricing for Green Metaverses: A Stackelberg Game Approach
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Metaverse enables users to communicate, collaborate and socialize with each other through their digital avatars. Due to the spatio-temporal characteristics, co-located users are served well by performing their software components in a collaborative manner such that a Metaverse service provider (MSP) eliminates redundant data transmission and processing, ultimately reducing the total energy consumption. The energyefficient service provision is crucial for enabling the green and sustainable Metaverse. In this article, we take an augmented reality (AR) application as an example to achieve this goal. Moreover, we study an economic issue on how the users reserve offloading services from the MSP and how the MSP determines an optimal charging price since each user is rational to decide whether to accept the offloading service by taking into account the monetary cost. A single-leader multi-follower Stackelberg game is formulated between the MSP and users while each user optimizes an offloading probability to minimize the weighted sum of time, energy consumption and monetary cost. Numerical results show that our scheme achieves energy savings and satisfies individual rationality simultaneously compared with the conventional schemes. Finally, we identify and discuss open directions on how several emerging technologies are combined with the sustainable green Metaverse.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 12:27:49 GMT" } ]
1,691,625,600,000
[ [ "Huang", "Xumin", "" ], [ "Wu", "Yuan", "" ], [ "Kang", "Jiawen", "" ], [ "Nie", "Jiangtian", "" ], [ "Zhong", "Weifeng", "" ], [ "Kim", "Dong In", "" ], [ "Xie", "Shengli", "" ] ]
2308.05012
Awad Abdelhalim
Michael Leong, Awad Abdelhalim, Jude Ha, Dianne Patterson, Gabriel L. Pincus, Anthony B. Harris, Michael Eichler, Jinhua Zhao
MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Transit riders' feedback provided in ridership surveys, customer relationship management (CRM) channels, and in more recent times, through social media is key for transit agencies to better gauge the efficacy of their services and initiatives. Getting a holistic understanding of riders' experience through the feedback shared in those instruments is often challenging, mostly due to the open-ended, unstructured nature of text feedback. In this paper, we propose leveraging traditional transit CRM feedback to develop and deploy a transit-topic-aware large language model (LLM) capable of classifying open-ended text feedback to relevant transit-specific topics. First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority (WMATA). We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. We compare our LLM, MetRoBERTa, to classical machine learning approaches utilizing keyword-based and lexicon representations. Our model outperforms those methods across all evaluation metrics, providing an average topic classification accuracy of 90%. Finally, we provide a value proposition of this work demonstrating how the language model, alongside additional text processing tools, can be applied to add structure to open-ended text sources of feedback like Twitter. The framework and results we present provide a pathway for an automated, generalizable approach for ingesting, visualizing, and reporting transit riders' feedback at scale, enabling agencies to better understand and improve customer experience.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 15:11:37 GMT" } ]
1,691,625,600,000
[ [ "Leong", "Michael", "" ], [ "Abdelhalim", "Awad", "" ], [ "Ha", "Jude", "" ], [ "Patterson", "Dianne", "" ], [ "Pincus", "Gabriel L.", "" ], [ "Harris", "Anthony B.", "" ], [ "Eichler", "Michael", "" ], [ "Zhao", "Jinhua", "" ] ]
2308.05062
Holger Hoos
Chris Fawcett, Mauro Vallati, Holger H. Hoos, Alfonso E. Gerevini
Competitions in AI -- Robustly Ranking Solvers Using Statistical Resampling
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Solver competitions play a prominent role in assessing and advancing the state of the art for solving many problems in AI and beyond. Notably, in many areas of AI, competitions have had substantial impact in guiding research and applications for many years, and for a solver to be ranked highly in a competition carries considerable weight. But to which extent can we expect competition results to generalise to sets of problem instances different from those used in a particular competition? This is the question we investigate here, using statistical resampling techniques. We show that the rankings resulting from the standard interpretation of competition results can be very sensitive to even minor changes in the benchmark instance set used as the basis for assessment and can therefore not be expected to carry over to other samples from the same underlying instance distribution. To address this problem, we introduce a novel approach to statistically meaningful analysis of competition results based on resampling performance data. Our approach produces confidence intervals of competition scores as well as statistically robust solver rankings with bounded error. Applied to recent SAT, AI planning and computer vision competitions, our analysis reveals frequent statistical ties in solver performance as well as some inversions of ranks compared to the official results based on simple scoring.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 16:47:04 GMT" } ]
1,691,625,600,000
[ [ "Fawcett", "Chris", "" ], [ "Vallati", "Mauro", "" ], [ "Hoos", "Holger H.", "" ], [ "Gerevini", "Alfonso E.", "" ] ]
2308.05385
Tao Zou
Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang
Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification
13 pages
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus on analyzing the text descriptions of patents. However, apart from the texts, each patent is also associated with some assignees, and the knowledge of their applied patents is often valuable for classification. Furthermore, the hierarchical taxonomy formulated by the IPC system provides important contextual information and enables models to leverage the correlations between IPC codes for more accurate classification. However, existing methods fail to incorporate the above aspects. In this paper, we propose an integrated framework that comprehensively considers the information on patents for patent classification. To be specific, we first present an IPC codes correlations learning module to derive their semantic representations via adaptively passing and aggregating messages within the same level and across different levels along the hierarchical taxonomy. Moreover, we design a historical application patterns learning component to incorporate the corresponding assignee's previous patents by a dual channel aggregation mechanism. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions. Experiments on real-world datasets demonstrate the superiority of our approach over the existing methods. Besides, we present the model's ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 07:02:24 GMT" } ]
1,691,712,000,000
[ [ "Zou", "Tao", "" ], [ "Yu", "Le", "" ], [ "Sun", "Leilei", "" ], [ "Du", "Bowen", "" ], [ "Wang", "Deqing", "" ], [ "Zhuang", "Fuzhen", "" ] ]
2308.05391
Segev Shlomov
Sivan Schwartz, Avi Yaeli, Segev Shlomov
Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future Challenges
Accepted to the First International Workshop on the Future of No-Code Digital Apprentices
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Trust in AI agents has been extensively studied in the literature, resulting in significant advancements in our understanding of this field. However, the rapid advancements in Large Language Models (LLMs) and the emergence of LLM-based AI agent frameworks pose new challenges and opportunities for further research. In the field of process automation, a new generation of AI-based agents has emerged, enabling the execution of complex tasks. At the same time, the process of building automation has become more accessible to business users via user-friendly no-code tools and training mechanisms. This paper explores these new challenges and opportunities, analyzes the main aspects of trust in AI agents discussed in existing literature, and identifies specific considerations and challenges relevant to this new generation of automation agents. We also evaluate how nascent products in this category address these considerations. Finally, we highlight several challenges that the research community should address in this evolving landscape.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 07:12:11 GMT" } ]
1,691,712,000,000
[ [ "Schwartz", "Sivan", "" ], [ "Yaeli", "Avi", "" ], [ "Shlomov", "Segev", "" ] ]
2308.05501
Sapir Gershov
Sapir Gershov, Fadi Mahameed, Aeyal Raz, Shlomi Laufer
More Than Meets the Eye: Analyzing Anesthesiologists' Visual Attention in the Operating Room Using Deep Learning Models
Submitted to MICCAI Aml4HC 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Patient's vital signs, which are displayed on monitors, make the anesthesiologist's visual attention (VA) a key component in the safe management of patients under general anesthesia; moreover, the distribution of said VA and the ability to acquire specific cues throughout the anesthetic, may have a direct impact on patient's outcome. Currently, most studies employ wearable eye-tracking technologies to analyze anesthesiologists' visual patterns. Albeit being able to produce meticulous data, wearable devices are not a sustainable solution for large-scale or long-term use for data collection in the operating room (OR). Thus, by utilizing a novel eye-tracking method in the form of deep learning models that process monitor-mounted webcams, we collected continuous behavioral data and gained insight into the anesthesiologist's VA distribution with minimal disturbance to their natural workflow. In this study, we collected OR video recordings using the proposed framework and compared different visual behavioral patterns. We distinguished between baseline VA distribution during uneventful periods to patterns associated with active phases or during critical, unanticipated incidents. In the future, such a platform may serve as a crucial component of context-aware assistive technologies in the OR.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 11:12:04 GMT" } ]
1,691,712,000,000
[ [ "Gershov", "Sapir", "" ], [ "Mahameed", "Fadi", "" ], [ "Raz", "Aeyal", "" ], [ "Laufer", "Shlomi", "" ] ]
2308.05567
Pan Liang
Pan Liang, Danwei Ye, Zihao Zhu, Yunchao Wang, Wang Xia, Ronghua Liang, and Guodao Sun
C5: Towards Better Conversation Comprehension and Contextual Continuity for ChatGPT
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Large language models (LLMs), such as ChatGPT, have demonstrated outstanding performance in various fields, particularly in natural language understanding and generation tasks. In complex application scenarios, users tend to engage in multi-turn conversations with ChatGPT to keep contextual information and obtain comprehensive responses. However, human forgetting and model contextual forgetting remain prominent issues in multi-turn conversation scenarios, which challenge the users' conversation comprehension and contextual continuity for ChatGPT. To address these challenges, we propose an interactive conversation visualization system called C5, which includes Global View, Topic View, and Context-associated Q\&A View. The Global View uses the GitLog diagram metaphor to represent the conversation structure, presenting the trend of conversation evolution and supporting the exploration of locally salient features. The Topic View is designed to display all the question and answer nodes and their relationships within a topic using the structure of a knowledge graph, thereby display the relevance and evolution of conversations. The Context-associated Q\&A View consists of three linked views, which allow users to explore individual conversations deeply while providing specific contextual information when posing questions. The usefulness and effectiveness of C5 were evaluated through a case study and a user study.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 13:29:12 GMT" } ]
1,691,712,000,000
[ [ "Liang", "Pan", "" ], [ "Ye", "Danwei", "" ], [ "Zhu", "Zihao", "" ], [ "Wang", "Yunchao", "" ], [ "Xia", "Wang", "" ], [ "Liang", "Ronghua", "" ], [ "Sun", "Guodao", "" ] ]
2308.05585
Miao Fan
Miao Fan, Chen Hu, Shuchang Zhou
Proximal Policy Optimization Actual Combat: Manipulating Output Tokenizer Length
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The Reinforcement Learning from Human Feedback (RLHF) plays a pivotal role in shaping the impact of large language models (LLMs), contributing significantly to controlling output toxicity and selecting output styles, particularly as LLMs often harbor misleading content, highlighting the urgency to align them with human values for secure AI systems. The RLHF, characterized by complexity, instability, and sensitivity to hyperparameters, makes the evaluation of the reward model for complex tasks challenging, thereby further complicating the use of Proximal Policy Optimization (PPO). In this paper, we introduce a simple task designed to employ Gloden as a reward model that validates the effectiveness of PPO and inspires it, primarily explaining the task of utilizing PPO to manipulate the tokenizer length of the output generated by the model. Experiments confirm that PPO is not only effective in manipulating the output tokenizer length to a certain extent in this type of task but also exhibits facilitated training once the influence of the reward model effect is excluded, making it an exciting development.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 13:50:17 GMT" } ]
1,691,712,000,000
[ [ "Fan", "Miao", "" ], [ "Hu", "Chen", "" ], [ "Zhou", "Shuchang", "" ] ]
2308.05617
Hanzhao Wang
Hanzhao Wang, Zhongze Cai, Xiaocheng Li, Kalyan Talluri
A Neural Network Based Choice Model for Assortment Optimization
arXiv admin note: substantial text overlap with arXiv:2208.09325
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Discrete-choice models are used in economics, marketing and revenue management to predict customer purchase probabilities, say as a function of prices and other features of the offered assortment. While they have been shown to be expressive, capturing customer heterogeneity and behaviour, they are also hard to estimate, often based on many unobservables like utilities; and moreover, they still fail to capture many salient features of customer behaviour. A natural question then, given their success in other contexts, is if neural networks can eliminate the necessity of carefully building a context-dependent customer behaviour model and hand-coding and tuning the estimation. It is unclear however how one would incorporate assortment effects into such a neural network, and also how one would optimize the assortment with such a black-box generative model of choice probabilities. In this paper we investigate first whether a single neural network architecture can predict purchase probabilities for datasets from various contexts and generated under various models and assumptions. Next, we develop an assortment optimization formulation that is solvable by off-the-shelf integer programming solvers. We compare against a variety of benchmark discrete-choice models on simulated as well as real-world datasets, developing training tricks along the way to make the neural network prediction and subsequent optimization robust and comparable in performance to the alternates.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 15:01:52 GMT" } ]
1,691,712,000,000
[ [ "Wang", "Hanzhao", "" ], [ "Cai", "Zhongze", "" ], [ "Li", "Xiaocheng", "" ], [ "Talluri", "Kalyan", "" ] ]
2308.05780
Rohan Gupta
Sidhantha Poddar and Rohan Gupta
Optical Script Identification for multi-lingual Indic-script
20 pages , 12 figures Keywords: Optical character Identification, Pre-processing, feature extraction, multi-script, Indic-script, Script Recognition
null
null
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
Script identification and text recognition are some of the major domains in the application of Artificial Intelligence. In this era of digitalization, the use of digital note-taking has become a common practice. Still, conventional methods of using pen and paper is a prominent way of writing. This leads to the classification of scripts based on the method they are obtained. A survey on the current methodologies and state-of-art methods used for processing and identification would prove beneficial for researchers. The aim of this article is to discuss the advancement in the techniques for script pre-processing and text recognition. In India there are twelve prominent Indic scripts, unlike the English language, these scripts have layers of characteristics. Complex characteristics such as similarity in text shape make them difficult to recognize and analyze, thus this requires advance preprocessing methods for their accurate recognition. A sincere attempt is made in this survey to provide a comparison between all algorithms. We hope that this survey would provide insight to a researcher working not only on Indic scripts but also other languages.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 14:02:05 GMT" } ]
1,691,971,200,000
[ [ "Poddar", "Sidhantha", "" ], [ "Gupta", "Rohan", "" ] ]
2308.05960
Zhiwei Liu
Zhiwei Liu, Weiran Yao, Jianguo Zhang, Le Xue, Shelby Heinecke, Rithesh Murthy, Yihao Feng, Zeyuan Chen, Juan Carlos Niebles, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
Preprint
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs). An LAA is able to generate actions with its core LLM and interact with environments, which facilitates the ability to resolve complex tasks by conditioning on past interactions such as observations and actions. Since the investigation of LAA is still very recent, limited explorations are available. Therefore, we provide a comprehensive comparison of LAA in terms of both agent architectures and LLM backbones. Additionally, we propose a new strategy to orchestrate multiple LAAs such that each labor LAA focuses on one type of action, \textit{i.e.} BOLAA, where a controller manages the communication among multiple agents. We conduct simulations on both decision-making and multi-step reasoning environments, which comprehensively justify the capacity of LAAs. Our performance results provide quantitative suggestions for designing LAA architectures and the optimal choice of LLMs, as well as the compatibility of both. We release our implementation code of LAAs to the public at \url{https://github.com/salesforce/BOLAA}.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 06:37:54 GMT" } ]
1,691,971,200,000
[ [ "Liu", "Zhiwei", "" ], [ "Yao", "Weiran", "" ], [ "Zhang", "Jianguo", "" ], [ "Xue", "Le", "" ], [ "Heinecke", "Shelby", "" ], [ "Murthy", "Rithesh", "" ], [ "Feng", "Yihao", "" ], [ "Chen", "Zeyuan", "" ], [ "Niebles", "Juan Carlos", "" ], [ "Arpit", "Devansh", "" ], [ "Xu", "Ran", "" ], [ "Mui", "Phil", "" ], [ "Wang", "Huan", "" ], [ "Xiong", "Caiming", "" ], [ "Savarese", "Silvio", "" ] ]
2308.05984
Alberto Pozanco
Parisa Zehtabi, Alberto Pozanco, Ayala Bloch, Daniel Borrajo, Sarit Kraus
Contrastive Explanations of Centralized Multi-agent Optimization Solutions
Paper accepted at ICAPS 2024. This is a extended version that includes Supplementary Material
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In many real-world scenarios, agents are involved in optimization problems. Since most of these scenarios are over-constrained, optimal solutions do not always satisfy all agents. Some agents might be unhappy and ask questions of the form ``Why does solution $S$ not satisfy property $P$?''. We propose CMAoE, a domain-independent approach to obtain contrastive explanations by: (i) generating a new solution $S^\prime$ where property $P$ is enforced, while also minimizing the differences between $S$ and $S^\prime$; and (ii) highlighting the differences between the two solutions, with respect to the features of the objective function of the multi-agent system. Such explanations aim to help agents understanding why the initial solution is better in the context of the multi-agent system than what they expected. We have carried out a computational evaluation that shows that CMAoE can generate contrastive explanations for large multi-agent optimization problems. We have also performed an extensive user study in four different domains that shows that: (i) after being presented with these explanations, humans' satisfaction with the original solution increases; and (ii) the constrastive explanations generated by CMAoE are preferred or equally preferred by humans over the ones generated by state of the art approaches.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 07:42:17 GMT" }, { "version": "v2", "created": "Wed, 13 Mar 2024 13:56:05 GMT" } ]
1,710,374,400,000
[ [ "Zehtabi", "Parisa", "" ], [ "Pozanco", "Alberto", "" ], [ "Bloch", "Ayala", "" ], [ "Borrajo", "Daniel", "" ], [ "Kraus", "Sarit", "" ] ]
2308.05985
Liang Zhang
Liang Zhang, Nathaniel Xu, Pengfei Yang, Gaojie Jin, Cheng-Chao Huang, Lijun Zhang
TrajPAC: Towards Robustness Verification of Pedestrian Trajectory Prediction Models
ICCV 2023 version
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Robust pedestrian trajectory forecasting is crucial to developing safe autonomous vehicles. Although previous works have studied adversarial robustness in the context of trajectory forecasting, some significant issues remain unaddressed. In this work, we try to tackle these crucial problems. Firstly, the previous definitions of robustness in trajectory prediction are ambiguous. We thus provide formal definitions for two kinds of robustness, namely label robustness and pure robustness. Secondly, as previous works fail to consider robustness about all points in a disturbance interval, we utilise a probably approximately correct (PAC) framework for robustness verification. Additionally, this framework can not only identify potential counterexamples, but also provides interpretable analyses of the original methods. Our approach is applied using a prototype tool named TrajPAC. With TrajPAC, we evaluate the robustness of four state-of-the-art trajectory prediction models -- Trajectron++, MemoNet, AgentFormer, and MID -- on trajectories from five scenes of the ETH/UCY dataset and scenes of the Stanford Drone Dataset. Using our framework, we also experimentally study various factors that could influence robustness performance.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 07:43:00 GMT" } ]
1,691,971,200,000
[ [ "Zhang", "Liang", "" ], [ "Xu", "Nathaniel", "" ], [ "Yang", "Pengfei", "" ], [ "Jin", "Gaojie", "" ], [ "Huang", "Cheng-Chao", "" ], [ "Zhang", "Lijun", "" ] ]
2308.05996
Qi Liu
Qi Liu, Zhilong Zhou, Gangwei Jiang, Tiezheng Ge, Defu Lian
Deep Task-specific Bottom Representation Network for Multi-Task Recommendation
CIKM'23
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Neural-based multi-task learning (MTL) has gained significant improvement, and it has been successfully applied to recommendation system (RS). Recent deep MTL methods for RS (e.g. MMoE, PLE) focus on designing soft gating-based parameter-sharing networks that implicitly learn a generalized representation for each task. However, MTL methods may suffer from performance degeneration when dealing with conflicting tasks, as negative transfer effects can occur on the task-shared bottom representation. This can result in a reduced capacity for MTL methods to capture task-specific characteristics, ultimately impeding their effectiveness and hindering the ability to generalize well on all tasks. In this paper, we focus on the bottom representation learning of MTL in RS and propose the Deep Task-specific Bottom Representation Network (DTRN) to alleviate the negative transfer problem. DTRN obtains task-specific bottom representation explicitly by making each task have its own representation learning network in the bottom representation modeling stage. Specifically, it extracts the user's interests from multiple types of behavior sequences for each task through the parameter-efficient hypernetwork. To further obtain the dedicated representation for each task, DTRN refines the representation of each feature by employing a SENet-like network for each task. The two proposed modules can achieve the purpose of getting task-specific bottom representation to relieve tasks' mutual interference. Moreover, the proposed DTRN is flexible to combine with existing MTL methods. Experiments on one public dataset and one industrial dataset demonstrate the effectiveness of the proposed DTRN.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 08:04:43 GMT" }, { "version": "v2", "created": "Fri, 18 Aug 2023 01:37:14 GMT" } ]
1,692,576,000,000
[ [ "Liu", "Qi", "" ], [ "Zhou", "Zhilong", "" ], [ "Jiang", "Gangwei", "" ], [ "Ge", "Tiezheng", "" ], [ "Lian", "Defu", "" ] ]
2308.06088
Arne Bewersdorff
Arne Bewersdorff, Kathrin Se{\ss}ler, Armin Baur, Enkelejda Kasneci, Claudia Nerdel
Assessing Student Errors in Experimentation Using Artificial Intelligence and Large Language Models: A Comparative Study with Human Raters
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifying logical errors in complex, incomplete or even contradictory and overall heterogeneous data like students' experimentation protocols is challenging. Recognizing the limitations of current evaluation methods, we investigate the potential of Large Language Models (LLMs) for automatically identifying student errors and streamlining teacher assessments. Our aim is to provide a foundation for productive, personalized feedback. Using a dataset of 65 student protocols, an Artificial Intelligence (AI) system based on the GPT-3.5 and GPT-4 series was developed and tested against human raters. Our results indicate varying levels of accuracy in error detection between the AI system and human raters. The AI system can accurately identify many fundamental student errors, for instance, the AI system identifies when a student is focusing the hypothesis not on the dependent variable but solely on an expected observation (acc. = 0.90), when a student modifies the trials in an ongoing investigation (acc. = 1), and whether a student is conducting valid test trials (acc. = 0.82) reliably. The identification of other, usually more complex errors, like whether a student conducts a valid control trial (acc. = .60), poses a greater challenge. This research explores not only the utility of AI in educational settings, but also contributes to the understanding of the capabilities of LLMs in error detection in inquiry-based learning like experimentation.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 12:03:12 GMT" } ]
1,691,971,200,000
[ [ "Bewersdorff", "Arne", "" ], [ "Seßler", "Kathrin", "" ], [ "Baur", "Armin", "" ], [ "Kasneci", "Enkelejda", "" ], [ "Nerdel", "Claudia", "" ] ]
2308.06137
Kushal Kedia
Kushal Kedia, Prithwish Dan, Sanjiban Choudhury
A Game-Theoretic Framework for Joint Forecasting and Planning
IROS 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
Planning safe robot motions in the presence of humans requires reliable forecasts of future human motion. However, simply predicting the most likely motion from prior interactions does not guarantee safety. Such forecasts fail to model the long tail of possible events, which are rarely observed in limited datasets. On the other hand, planning for worst-case motions leads to overtly conservative behavior and a "frozen robot". Instead, we aim to learn forecasts that predict counterfactuals that humans guard against. We propose a novel game-theoretic framework for joint planning and forecasting with the payoff being the performance of the planner against the demonstrator, and present practical algorithms to train models in an end-to-end fashion. We demonstrate that our proposed algorithm results in safer plans in a crowd navigation simulator and real-world datasets of pedestrian motion. We release our code at https://github.com/portal-cornell/Game-Theoretic-Forecasting-Planning.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 13:56:39 GMT" }, { "version": "v2", "created": "Fri, 20 Oct 2023 03:40:56 GMT" } ]
1,698,019,200,000
[ [ "Kedia", "Kushal", "" ], [ "Dan", "Prithwish", "" ], [ "Choudhury", "Sanjiban", "" ] ]
2308.06922
Peng Zhao
Peng Zhao
Probabilistic contingent planning based on HTN for high-quality plans
10 pages, 1 figure
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Deterministic planning assumes that the planning evolves along a fully predictable path, and therefore it loses the practical value in most real projections. A more realistic view is that planning ought to take into consideration partial observability beforehand and aim for a more flexible and robust solution. What is more significant, it is inevitable that the quality of plan varies dramatically in the partially observable environment. In this paper we propose a probabilistic contingent Hierarchical Task Network (HTN) planner, named High-Quality Contingent Planner (HQCP), to generate high-quality plans in the partially observable environment. The formalisms in HTN planning are extended into partial observability and are evaluated regarding the cost. Next, we explore a novel heuristic for high-quality plans and develop the integrated planning algorithm. Finally, an empirical study verifies the effectiveness and efficiency of the planner both in probabilistic contingent planning and for obtaining high-quality plans.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 03:55:14 GMT" }, { "version": "v2", "created": "Thu, 28 Sep 2023 06:53:01 GMT" } ]
1,695,945,600,000
[ [ "Zhao", "Peng", "" ] ]
2308.07307
Yuhe Nie
Yuhe Nie, Shaoming Zheng, Zhan Zhuang, Xuan Song
Extend Wave Function Collapse to Large-Scale Content Generation
This paper is accepted by IEEE Conference on Games 2023 (nomination of the Best Paper Award)
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC) algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and sub-complete tileset preparation strategy, which requires only a small number of tiles to generate aperiodic and deterministic infinite content. We also introduce the weight-brush system that combines N-WFC and sub-complete tileset, proving its suitability for game design. Our contribution addresses WFC's challenge in massive content generation and provides a theoretical basis for implementing concrete games.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 17:50:38 GMT" } ]
1,692,057,600,000
[ [ "Nie", "Yuhe", "" ], [ "Zheng", "Shaoming", "" ], [ "Zhuang", "Zhan", "" ], [ "Song", "Xuan", "" ] ]
2308.07322
Robert Burdett
Robert L Burdett, Paul Corry, Prasad Yarlagadda, David Cook, Sean Birgan
Multicriteria Optimization Techniques for Understanding the Case Mix Landscape of a Hospital
38 pages, 17 figures, 11 tables
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Various medical and surgical units operate in a typical hospital and to treat their patients these units compete for infrastructure like operating rooms (OR) and ward beds. How that competition is regulated affects the capacity and output of a hospital. This article considers the impact of treating different patient case mix (PCM) in a hospital. As each case mix has an economic consequence and a unique profile of hospital resource usage, this consideration is important. To better understand the case mix landscape and to identify those which are optimal from a capacity utilisation perspective, an improved multicriteria optimization (MCO) approach is proposed. As there are many patient types in a typical hospital, the task of generating an archive of non-dominated (i.e., Pareto optimal) case mix is computationally challenging. To generate a better archive, an improved parallelised epsilon constraint method (ECM) is introduced. Our parallel random corrective approach is significantly faster than prior methods and is not restricted to evaluating points on a structured uniform mesh. As such we can generate more solutions. The application of KD-Trees is another new contribution. We use them to perform proximity testing and to store the high dimensional Pareto frontier (PF). For generating, viewing, navigating, and querying an archive, the development of a suitable decision support tool (DST) is proposed and demonstrated.
[ { "version": "v1", "created": "Mon, 31 Jul 2023 22:55:48 GMT" } ]
1,692,144,000,000
[ [ "Burdett", "Robert L", "" ], [ "Corry", "Paul", "" ], [ "Yarlagadda", "Prasad", "" ], [ "Cook", "David", "" ], [ "Birgan", "Sean", "" ] ]
2308.07327
Juho Kim
Juho Kim
PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations
8 pages, 1 figure, submission to IEEE Transactions on Games
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games. The source code is available at https://github.com/uoftcprg/pokerkit
[ { "version": "v1", "created": "Tue, 8 Aug 2023 13:54:48 GMT" }, { "version": "v2", "created": "Sun, 10 Sep 2023 22:20:32 GMT" }, { "version": "v3", "created": "Tue, 3 Oct 2023 23:42:04 GMT" }, { "version": "v4", "created": "Wed, 11 Oct 2023 06:34:56 GMT" }, { "version": "v5", "created": "Mon, 16 Oct 2023 14:33:02 GMT" } ]
1,697,500,800,000
[ [ "Kim", "Juho", "" ] ]
2308.07457
Michael Wilbur
Michael Wilbur, Amutheezan Sivagnanam, Afiya Ayman, Samitha Samaranayeke, Abhishek Dubey, Aron Laszka
Artificial Intelligence for Smart Transportation
This is a pre-print for a book chapter to appear in Vorobeychik, Yevgeniy., and Mukhopadhyay, Ayan., (Eds.). (2023). Artificial Intelligence and Society. ACM Press
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year. A well-functioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society. Since affordable public transit services are the backbones of many communities, this work investigates ways in which Artificial Intelligence (AI) can improve efficiency and increase utilization from the perspective of transit agencies. This book chapter discusses the primary requirements, objectives, and challenges related to the design of AI-driven smart transportation systems. We focus on three major topics. First, we discuss data sources and data. Second, we provide an overview of how AI can aid decision-making with a focus on transportation. Lastly, we discuss computational problems in the transportation domain and AI approaches to these problems.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 21:01:00 GMT" } ]
1,692,144,000,000
[ [ "Wilbur", "Michael", "" ], [ "Sivagnanam", "Amutheezan", "" ], [ "Ayman", "Afiya", "" ], [ "Samaranayeke", "Samitha", "" ], [ "Dubey", "Abhishek", "" ], [ "Laszka", "Aron", "" ] ]
2308.07738
Debraj Chakraborty
Debraj Chakraborty, Damien Busatto-Gaston, Jean-Fran\c{c}ois Raskin and Guillermo A. P\'erez
Formally-Sharp DAgger for MCTS: Lower-Latency Monte Carlo Tree Search using Data Aggregation with Formal Methods
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We study how to efficiently combine formal methods, Monte Carlo Tree Search (MCTS), and deep learning in order to produce high-quality receding horizon policies in large Markov Decision processes (MDPs). In particular, we use model-checking techniques to guide the MCTS algorithm in order to generate offline samples of high-quality decisions on a representative set of states of the MDP. Those samples can then be used to train a neural network that imitates the policy used to generate them. This neural network can either be used as a guide on a lower-latency MCTS online search, or alternatively be used as a full-fledged policy when minimal latency is required. We use statistical model checking to detect when additional samples are needed and to focus those additional samples on configurations where the learnt neural network policy differs from the (computationally-expensive) offline policy. We illustrate the use of our method on MDPs that model the Frozen Lake and Pac-Man environments -- two popular benchmarks to evaluate reinforcement-learning algorithms.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 12:33:58 GMT" } ]
1,692,144,000,000
[ [ "Chakraborty", "Debraj", "" ], [ "Busatto-Gaston", "Damien", "" ], [ "Raskin", "Jean-François", "" ], [ "Pérez", "Guillermo A.", "" ] ]
2308.08307
Toon Van de Maele
Toon Van de Maele, Bart Dhoedt, Tim Verbelen, Giovanni Pezzulo
Integrating cognitive map learning and active inference for planning in ambiguous environments
Accepted at IWAI 2023 (International Workshop on Active Inference)
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Living organisms need to acquire both cognitive maps for learning the structure of the world and planning mechanisms able to deal with the challenges of navigating ambiguous environments. Although significant progress has been made in each of these areas independently, the best way to integrate them is an open research question. In this paper, we propose the integration of a statistical model of cognitive map formation within an active inference agent that supports planning under uncertainty. Specifically, we examine the clone-structured cognitive graph (CSCG) model of cognitive map formation and compare a naive clone graph agent with an active inference-driven clone graph agent, in three spatial navigation scenarios. Our findings demonstrate that while both agents are effective in simple scenarios, the active inference agent is more effective when planning in challenging scenarios, in which sensory observations provide ambiguous information about location.
[ { "version": "v1", "created": "Wed, 16 Aug 2023 12:10:23 GMT" } ]
1,692,230,400,000
[ [ "Van de Maele", "Toon", "" ], [ "Dhoedt", "Bart", "" ], [ "Verbelen", "Tim", "" ], [ "Pezzulo", "Giovanni", "" ] ]
2308.09267
Lang Cao
Lang Cao
GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Large Language Models (LLMs) have showcased impressive reasoning capabilities, particularly when guided by specifically designed prompts in complex reasoning tasks such as math word problems. These models typically solve tasks using a chain-of-thought approach, which not only bolsters their reasoning abilities but also provides valuable insights into their problem-solving process. However, there is still significant room for enhancing the reasoning abilities of LLMs. Some studies suggest that the integration of an LLM output verifier can boost reasoning accuracy without necessitating additional model training. In this paper, we follow these studies and introduce a novel graph-based method to further augment the reasoning capabilities of LLMs. We posit that multiple solutions to a reasoning task, generated by an LLM, can be represented as a reasoning graph due to the logical connections between intermediate steps from different reasoning paths. Therefore, we propose the Reasoning Graph Verifier (GraphReason) to analyze and verify the solutions generated by LLMs. By evaluating these graphs, models can yield more accurate and reliable results.Our experimental results show that our graph-based verification method not only significantly enhances the reasoning abilities of LLMs but also outperforms existing verifier methods in terms of improving these models' reasoning performance.
[ { "version": "v1", "created": "Fri, 18 Aug 2023 03:12:59 GMT" }, { "version": "v2", "created": "Mon, 21 Aug 2023 05:24:34 GMT" }, { "version": "v3", "created": "Thu, 28 Sep 2023 16:35:58 GMT" }, { "version": "v4", "created": "Sun, 21 Apr 2024 01:45:34 GMT" } ]
1,713,830,400,000
[ [ "Cao", "Lang", "" ] ]
2308.09595
Muhammad Arrasy Rahman
Arrasy Rahman, Jiaxun Cui, Peter Stone
Minimum Coverage Sets for Training Robust Ad Hoc Teamwork Agents
Accepted at AAAI-24 conference
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Robustly cooperating with unseen agents and human partners presents significant challenges due to the diverse cooperative conventions these partners may adopt. Existing Ad Hoc Teamwork (AHT) methods address this challenge by training an agent with a population of diverse teammate policies obtained through maximizing specific diversity metrics. However, prior heuristic-based diversity metrics do not always maximize the agent's robustness in all cooperative problems. In this work, we first propose that maximizing an AHT agent's robustness requires it to emulate policies in the minimum coverage set (MCS), the set of best-response policies to any partner policies in the environment. We then introduce the L-BRDiv algorithm that generates a set of teammate policies that, when used for AHT training, encourage agents to emulate policies from the MCS. L-BRDiv works by solving a constrained optimization problem to jointly train teammate policies for AHT training and approximating AHT agent policies that are members of the MCS. We empirically demonstrate that L-BRDiv produces more robust AHT agents than state-of-the-art methods in a broader range of two-player cooperative problems without the need for extensive hyperparameter tuning for its objectives. Our study shows that L-BRDiv outperforms the baseline methods by prioritizing discovering distinct members of the MCS instead of repeatedly finding redundant policies.
[ { "version": "v1", "created": "Fri, 18 Aug 2023 14:45:22 GMT" }, { "version": "v2", "created": "Wed, 3 Jan 2024 03:05:25 GMT" } ]
1,704,326,400,000
[ [ "Rahman", "Arrasy", "" ], [ "Cui", "Jiaxun", "" ], [ "Stone", "Peter", "" ] ]
2308.09830
Oscar J. Romero
Oscar J. Romero, John Zimmerman, Aaron Steinfeld, Anthony Tomasic
Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis
AAAI 2023 Fall Symposium
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.
[ { "version": "v1", "created": "Fri, 18 Aug 2023 21:42:47 GMT" }, { "version": "v2", "created": "Tue, 5 Sep 2023 17:32:08 GMT" }, { "version": "v3", "created": "Thu, 28 Sep 2023 15:10:56 GMT" } ]
1,695,945,600,000
[ [ "Romero", "Oscar J.", "" ], [ "Zimmerman", "John", "" ], [ "Steinfeld", "Aaron", "" ], [ "Tomasic", "Anthony", "" ] ]
2308.10899
Tignting Liao
Tingting Liao, Hongwei Yi, Yuliang Xiu, Jiaxaing Tang, Yangyi Huang, Justus Thies, Michael J. Black
TADA! Text to Animatable Digital Avatars
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We introduce TADA, a simple-yet-effective approach that takes textual descriptions and produces expressive 3D avatars with high-quality geometry and lifelike textures, that can be animated and rendered with traditional graphics pipelines. Existing text-based character generation methods are limited in terms of geometry and texture quality, and cannot be realistically animated due to inconsistent alignment between the geometry and the texture, particularly in the face region. To overcome these limitations, TADA leverages the synergy of a 2D diffusion model and an animatable parametric body model. Specifically, we derive an optimizable high-resolution body model from SMPL-X with 3D displacements and a texture map, and use hierarchical rendering with score distillation sampling (SDS) to create high-quality, detailed, holistic 3D avatars from text. To ensure alignment between the geometry and texture, we render normals and RGB images of the generated character and exploit their latent embeddings in the SDS training process. We further introduce various expression parameters to deform the generated character during training, ensuring that the semantics of our generated character remain consistent with the original SMPL-X model, resulting in an animatable character. Comprehensive evaluations demonstrate that TADA significantly surpasses existing approaches on both qualitative and quantitative measures. TADA enables creation of large-scale digital character assets that are ready for animation and rendering, while also being easily editable through natural language. The code will be public for research purposes.
[ { "version": "v1", "created": "Mon, 21 Aug 2023 17:59:10 GMT" } ]
1,692,662,400,000
[ [ "Liao", "Tingting", "" ], [ "Yi", "Hongwei", "" ], [ "Xiu", "Yuliang", "" ], [ "Tang", "Jiaxaing", "" ], [ "Huang", "Yangyi", "" ], [ "Thies", "Justus", "" ], [ "Black", "Michael J.", "" ] ]
2308.10988
Adel Ammar
Adel Ammar
ERA*: Enhanced Relaxed A* algorithm for Solving the Shortest Path Problem in Regular Grid Maps
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
This paper introduces a novel algorithm for solving the point-to-point shortest path problem in a static regular 8-neighbor connectivity (G8) grid. This algorithm can be seen as a generalization of Hadlock algorithm to G8 grids, and is shown to be theoretically equivalent to the relaxed $A^*$ ($RA^*$) algorithm in terms of the provided solution's path length, but with substantial time and memory savings, due to a completely different computation strategy, based on defining a set of lookup matrices. Through an experimental study on grid maps of various types and sizes (1290 runs on 43 maps), it is proven to be 2.25 times faster than $RA^*$ and 17 times faster than the original $A^*$, in average. Moreover, it is more memory-efficient, since it does not need to store a G score matrix.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 07:25:13 GMT" } ]
1,692,748,800,000
[ [ "Ammar", "Adel", "" ] ]
2308.11755
Raj Korpan
Raj Korpan
VBMO: Voting-Based Multi-Objective Path Planning
First International Workshop on Search and Planning with Complex Objectives (WoSePCO) at IJCAI'2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
This paper presents VBMO, the Voting-Based Multi-Objective path planning algorithm, that generates optimal single-objective plans, evaluates each of them with respect to the other objectives, and selects one with a voting mechanism. VBMO does not use hand-tuned weights, consider the multiple objectives at every step of search, or use an evolutionary algorithm. Instead, it considers how a plan that is optimal in one objective may perform well with respect to others. VBMO incorporates three voting mechanisms: range, Borda, and combined approval. Extensive evaluation in diverse and complex environments demonstrates the algorithm's ability to efficiently produce plans that satisfy multiple objectives.
[ { "version": "v1", "created": "Tue, 22 Aug 2023 19:51:48 GMT" } ]
1,692,835,200,000
[ [ "Korpan", "Raj", "" ] ]
2308.12411
Michael Hochberg
Michael E. Hochberg
A Theory of Intelligences
37 pages, 1 Table, 6 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Intelligence is a human construct to represent the ability to achieve goals. Given this wide berth, intelligence has been defined countless times, studied in a variety of ways and represented using numerous measures. Understanding intelligence ultimately requires theory and quantification, both of which have proved elusive. I develop a framework -- the Theory of Intelligences (TIS) -- that applies across all systems from physics, to biology, humans and AI. TIS likens intelligence to a calculus, differentiating, correlating and integrating information. Intelligence operates at many levels and scales and TIS distils these into a parsimonious macroscopic framework centered on solving, planning and their optimization to accomplish goals. Notably, intelligence can be expressed in informational units or in units relative to goal difficulty, the latter defined as complexity relative to system (individual or benchmarked) ability. I present general equations for intelligence and its components, and a simple expression for the evolution of intelligence traits. The measures developed here could serve to gauge different facets of intelligence for any step-wise transformation of information. I argue that proxies such as environment, technology, society and collectives are essential to a general theory of intelligence and to possible evolutionary transitions in intelligence, particularly in humans. I conclude with testable predictions of TIS and offer several speculations.
[ { "version": "v1", "created": "Wed, 23 Aug 2023 20:18:43 GMT" }, { "version": "v2", "created": "Fri, 5 Apr 2024 21:36:17 GMT" } ]
1,712,620,800,000
[ [ "Hochberg", "Michael E.", "" ] ]
2308.12486
Bowen Xu
Bowen Xu
A Brain-Inspired Sequence Learning Model based on a Logic
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sequence learning is an essential aspect of intelligence. In Artificial Intelligence, sequence prediction task is usually used to test a sequence learning model. In this paper, a model of sequence learning, which is interpretable through Non-Axiomatic Logic, is designed and tested. The learning mechanism is composed of three steps, hypothesizing, revising, and recycling, which enable the model to work under the Assumption of Insufficient Knowledge and Resources. Synthetic datasets for sequence prediction task are generated to test the capacity of the model. The results show that the model works well within different levels of difficulty. In addition, since the model adopts concept-centered representation, it theoretically does not suffer from catastrophic forgetting, and the practical results also support this property. This paper shows the potential of learning sequences in a logical way.
[ { "version": "v1", "created": "Thu, 24 Aug 2023 01:01:41 GMT" }, { "version": "v2", "created": "Mon, 6 Nov 2023 16:26:09 GMT" } ]
1,699,315,200,000
[ [ "Xu", "Bowen", "" ] ]
2308.12682
Rishi Hazra
Rishi Hazra, Pedro Zuidberg Dos Martires, Luc De Raedt
SayCanPay: Heuristic Planning with Large Language Models using Learnable Domain Knowledge
Accepted in AAAI 2024. Website: https://rishihazra.github.io/SayCanPay/
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Large Language Models (LLMs) have demonstrated impressive planning abilities due to their vast "world knowledge". Yet, obtaining plans that are both feasible (grounded in affordances) and cost-effective (in plan length), remains a challenge, despite recent progress. This contrasts with heuristic planning methods that employ domain knowledge (formalized in action models such as PDDL) and heuristic search to generate feasible, optimal plans. Inspired by this, we propose to combine the power of LLMs and heuristic planning by leveraging the world knowledge of LLMs and the principles of heuristic search. Our approach, SayCanPay, employs LLMs to generate actions (Say) guided by learnable domain knowledge, that evaluates actions' feasibility (Can) and long-term reward/payoff (Pay), and heuristic search to select the best sequence of actions. Our contributions are (1) a novel framing of the LLM planning problem in the context of heuristic planning, (2) integrating grounding and cost-effective elements into the generated plans, and (3) using heuristic search over actions. Our extensive evaluations show that our model surpasses other LLM planning approaches.
[ { "version": "v1", "created": "Thu, 24 Aug 2023 09:47:28 GMT" }, { "version": "v2", "created": "Mon, 1 Jan 2024 19:28:22 GMT" } ]
1,704,240,000,000
[ [ "Hazra", "Rishi", "" ], [ "Martires", "Pedro Zuidberg Dos", "" ], [ "De Raedt", "Luc", "" ] ]
2308.13147
Lyndon Benke
Lyndon Benke, Tim Miller, Michael Papasimeon, and Nir Lipovetzky
Diverse, Top-k, and Top-Quality Planning Over Simulators
This paper has been accepted at the 26th European Conference on Artificial Intelligence (ECAI 2023)
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems. Previously this area has been the domain of classical planners that require a symbolic model of the problem instance. This paper proposes a novel alternative approach that uses Monte Carlo Tree Search (MCTS), enabling application to problems for which only a black-box simulation model is available. We present a procedure for extracting bounded sets of plans from pre-generated search trees in best-first order, and a metric for evaluating the relative quality of paths through a search tree. We demonstrate this approach on a path-planning problem with hidden information, and suggest adaptations to the MCTS algorithm to increase the diversity of generated plans. Our results show that our method can generate diverse and high-quality plan sets in domains where classical planners are not applicable.
[ { "version": "v1", "created": "Fri, 25 Aug 2023 02:55:19 GMT" } ]
1,693,180,800,000
[ [ "Benke", "Lyndon", "" ], [ "Miller", "Tim", "" ], [ "Papasimeon", "Michael", "" ], [ "Lipovetzky", "Nir", "" ] ]
2308.13433
Tom Westermann
Tom Westermann, Milapji Singh Gill, Alexander Fay
Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Model-Based Anomaly Detection has been a successful approach to identify deviations from the expected behavior of Cyber-Physical Production Systems. Since manual creation of these models is a time-consuming process, it is advantageous to learn them from data and represent them in a generic formalism like timed automata. However, these models - and by extension, the detected anomalies - can be challenging to interpret due to a lack of additional information about the system. This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies. The authors additionally propose an ontology of the necessary concepts. The approach was validated on a five-tank mixing CPPS and was able to formally define both automata model as well as timing anomalies in automata execution.
[ { "version": "v1", "created": "Fri, 25 Aug 2023 15:25:57 GMT" } ]
1,693,180,800,000
[ [ "Westermann", "Tom", "" ], [ "Gill", "Milapji Singh", "" ], [ "Fay", "Alexander", "" ] ]
2308.13542
Thommen George Karimpanal
Thommen George Karimpanal, Laknath Buddhika Semage, Santu Rana, Hung Le, Truyen Tran, Sunil Gupta and Svetha Venkatesh
LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying
18 pages, 11 figures
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. For example, by observing a partial stack of cubes, LLMs can predict the correct sequence in which the remaining cubes should be stacked by extrapolating the observed patterns (e.g., cube sizes, colors or other attributes) in the partial stack. In this work, we introduce LaGR (Language-Guided Reinforcement learning), which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent, in order to subsequently guide the latter's training. However, as RL training is generally not sample-efficient, deploying this approach would inherently imply that the LLM be repeatedly queried for solutions; a process that can be expensive and infeasible. To address this issue, we introduce SEQ (sample efficient querying), where we simultaneously train a secondary RL agent to decide when the LLM should be queried for solutions. Specifically, we use the quality of the solutions emanating from the LLM as the reward to train this agent. We show that our proposed framework LaGR-SEQ enables more efficient primary RL training, while simultaneously minimizing the number of queries to the LLM. We demonstrate our approach on a series of tasks and highlight the advantages of our approach, along with its limitations and potential future research directions.
[ { "version": "v1", "created": "Mon, 21 Aug 2023 02:07:35 GMT" } ]
1,693,267,200,000
[ [ "Karimpanal", "Thommen George", "" ], [ "Semage", "Laknath Buddhika", "" ], [ "Rana", "Santu", "" ], [ "Le", "Hung", "" ], [ "Tran", "Truyen", "" ], [ "Gupta", "Sunil", "" ], [ "Venkatesh", "Svetha", "" ] ]
2308.13548
Arpan Tripathi
Ahad Shams, Douglas Summers-Stay, Arpan Tripathi, Vsevolod Metelsky, Alexandros Titonis, Karan Malhotra
Towards a Holodeck-style Simulation Game
18 pages, 11 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We introduce Infinitia, a simulation game system that uses generative image and language models at play time to reshape all aspects of the setting and NPCs based on a short description from the player, in a way similar to how settings are created on the fictional Holodeck. Building off the ideas of the Generative Agents paper, our system introduces gameplay elements, such as infinite generated fantasy worlds, controllability of NPC behavior, humorous dialogue, cost & time efficiency, collaboration between players and elements of non-determinism among in-game events. Infinitia is implemented in the Unity engine with a server-client architecture, facilitating the addition of exciting features by community developers in the future. Furthermore, it uses a multiplayer framework to allow humans to be present and interact in the simulation. The simulation will be available in open-alpha shortly at https://infinitia.ai/ and we are looking forward to building upon it with the community.
[ { "version": "v1", "created": "Tue, 22 Aug 2023 19:19:19 GMT" }, { "version": "v2", "created": "Tue, 12 Sep 2023 10:03:25 GMT" } ]
1,694,563,200,000
[ [ "Shams", "Ahad", "" ], [ "Summers-Stay", "Douglas", "" ], [ "Tripathi", "Arpan", "" ], [ "Metelsky", "Vsevolod", "" ], [ "Titonis", "Alexandros", "" ], [ "Malhotra", "Karan", "" ] ]
2308.13755
Bayu Trisedya
Bayu Distiawan Trisedya, Flora D Salim, Jeffrey Chan, Damiano Spina, Falk Scholer, Mark Sanderson
i-Align: an interpretable knowledge graph alignment model
Data Min Knowl Disc (2023)
null
10.1007/s10618-023-00963-3
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Knowledge graphs (KGs) are becoming essential resources for many downstream applications. However, their incompleteness may limit their potential. Thus, continuous curation is needed to mitigate this problem. One of the strategies to address this problem is KG alignment, i.e., forming a more complete KG by merging two or more KGs. This paper proposes i-Align, an interpretable KG alignment model. Unlike the existing KG alignment models, i-Align provides an explanation for each alignment prediction while maintaining high alignment performance. Experts can use the explanation to check the correctness of the alignment prediction. Thus, the high quality of a KG can be maintained during the curation process (e.g., the merging process of two KGs). To this end, a novel Transformer-based Graph Encoder (Trans-GE) is proposed as a key component of i-Align for aggregating information from entities' neighbors (structures). Trans-GE uses Edge-gated Attention that combines the adjacency matrix and the self-attention matrix to learn a gating mechanism to control the information aggregation from the neighboring entities. It also uses historical embeddings, allowing Trans-GE to be trained over mini-batches, or smaller sub-graphs, to address the scalability issue when encoding a large KG. Another component of i-Align is a Transformer encoder for aggregating entities' attributes. This way, i-Align can generate explanations in the form of a set of the most influential attributes/neighbors based on attention weights. Extensive experiments are conducted to show the power of i-Align. The experiments include several aspects, such as the model's effectiveness for aligning KGs, the quality of the generated explanations, and its practicality for aligning large KGs. The results show the effectiveness of i-Align in these aspects.
[ { "version": "v1", "created": "Sat, 26 Aug 2023 03:48:52 GMT" } ]
1,693,267,200,000
[ [ "Trisedya", "Bayu Distiawan", "" ], [ "Salim", "Flora D", "" ], [ "Chan", "Jeffrey", "" ], [ "Spina", "Damiano", "" ], [ "Scholer", "Falk", "" ], [ "Sanderson", "Mark", "" ] ]
2308.13871
Jiaxi Lv
Jiaxi Lv, Liang Zhang, Yi Huang, Jiancheng Huang, Shifeng Chen
Graph Edit Distance Learning via Different Attention
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Recently, more and more research has focused on using Graph Neural Networks (GNN) to solve the Graph Similarity Computation problem (GSC), i.e., computing the Graph Edit Distance (GED) between two graphs. These methods treat GSC as an end-to-end learnable task, and the core of their architecture is the feature fusion modules to interact with the features of two graphs. Existing methods consider that graph-level embedding is difficult to capture the differences in local small structures between two graphs, and thus perform fine-grained feature fusion on node-level embedding can improve the accuracy, but leads to greater time and memory consumption in the training and inference phases. However, this paper proposes a novel graph-level fusion module Different Attention (DiffAtt), and demonstrates that graph-level fusion embeddings can substantially outperform these complex node-level fusion embeddings. We posit that the relative difference structure of the two graphs plays an important role in calculating their GED values. To this end, DiffAtt uses the difference between two graph-level embeddings as an attentional mechanism to capture the graph structural difference of the two graphs. Based on DiffAtt, a new GSC method, named Graph Edit Distance Learning via Different Attention (REDRAFT), is proposed, and experimental results demonstrate that REDRAFT achieves state-of-the-art performance in 23 out of 25 metrics in five benchmark datasets. Especially on MSE, it respectively outperforms the second best by 19.9%, 48.8%, 29.1%, 31.6%, and 2.2%. Moreover, we propose a quantitative test Remaining Subgraph Alignment Test (RESAT) to verify that among all graph-level fusion modules, the fusion embedding generated by DiffAtt can best capture the structural differences between two graphs.
[ { "version": "v1", "created": "Sat, 26 Aug 2023 13:05:01 GMT" } ]
1,693,267,200,000
[ [ "Lv", "Jiaxi", "" ], [ "Zhang", "Liang", "" ], [ "Huang", "Yi", "" ], [ "Huang", "Jiancheng", "" ], [ "Chen", "Shifeng", "" ] ]
2308.14269
Elad Liebman
Elad Liebman, Peter Stone
Utilizing Mood-Inducing Background Music in Human-Robot Interaction
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Past research has clearly established that music can affect mood and that mood affects emotional and cognitive processing, and thus decision-making. It follows that if a robot interacting with a person needs to predict the person's behavior, knowledge of the music the person is listening to when acting is a potentially relevant feature. To date, however, there has not been any concrete evidence that a robot can improve its human-interactive decision-making by taking into account what the person is listening to. This research fills this gap by reporting the results of an experiment in which human participants were required to complete a task in the presence of an autonomous agent while listening to background music. Specifically, the participants drove a simulated car through an intersection while listening to music. The intersection was not empty, as another simulated vehicle, controlled autonomously, was also crossing the intersection in a different direction. Our results clearly indicate that such background information can be effectively incorporated in an agent's world representation in order to better predict people's behavior. We subsequently analyze how knowledge of music impacted both participant behavior and the resulting learned policy.\setcounter{footnote}{2}\footnote{An earlier version of part of the material in this paper appeared originally in the first author's Ph.D. Dissertation~\cite{liebman2020sequential} but it has not appeared in any pear-reviewed conference or journal.}
[ { "version": "v1", "created": "Mon, 28 Aug 2023 02:54:05 GMT" } ]
1,693,267,200,000
[ [ "Liebman", "Elad", "" ], [ "Stone", "Peter", "" ] ]
2308.14284
Longchao Da
Longchao Da, Minquan Gao, Hao Mei, Hua Wei
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
9 pages, 7 figures. Accepted to AAAI 2024
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion headaches. However, there still exist performance gaps when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulator and the real-world environments. The Large Language Models (LLMs) are trained on mass knowledge and proved to be equipped with astonishing inference abilities. In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation. Accepting the cloze prompt template, and then filling in the answer based on accessible context, the pre-trained LLM's inference ability is exploited and applied to understand how weather conditions, traffic states, and road types influence traffic dynamics, being aware of this, the policies' action is taken and grounded based on realistic dynamics, thus help the agent learn a more realistic policy. We conduct experiments using DQN to show the effectiveness of the proposed PromptGAT's ability in mitigating the performance gap from simulation to reality (sim-to-real).
[ { "version": "v1", "created": "Mon, 28 Aug 2023 03:49:13 GMT" }, { "version": "v2", "created": "Mon, 4 Sep 2023 22:31:44 GMT" }, { "version": "v3", "created": "Thu, 26 Oct 2023 02:15:31 GMT" }, { "version": "v4", "created": "Mon, 8 Jan 2024 10:03:06 GMT" }, { "version": "v5", "created": "Wed, 17 Jan 2024 21:30:16 GMT" }, { "version": "v6", "created": "Sat, 20 Jan 2024 09:41:55 GMT" } ]
1,706,054,400,000
[ [ "Da", "Longchao", "" ], [ "Gao", "Minquan", "" ], [ "Mei", "Hao", "" ], [ "Wei", "Hua", "" ] ]
2308.14301
Chirag Shah
Muhammad Rahman, Sachi Figliolini, Joyce Kim, Eivy Cedeno, Charles Kleier, Chirag Shah, Aman Chadha
Artificial Intelligence in Career Counseling: A Test Case with ResumAI
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
The rise of artificial intelligence (AI) has led to various means of integration of AI aimed to provide efficiency in tasks, one of which is career counseling. A key part of getting a job is having a solid resume that passes through the first round of programs and recruiters. It is difficult to find good resources or schedule an appointment with a career counselor to help with editing a resume for a specific role. With the rise of ChatGPT, Bard, and several other AI chat programs it is possible to provide specific, automated feedback on various concerns to suggest places for improvement within the context of career counseling. This paper begins with a quick literature review on the ethical considerations and limitations of AI in career counseling. The authors also have created their own website service, called ResumAI, to test and review the functionality of an AI career counselor. The findings of this study will contribute to the understanding of chat AI ResumAI reviewer programs and sites. The implications of the findings for the field of career counseling, AI development, and ethical practice will be discussed.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 04:35:20 GMT" } ]
1,693,267,200,000
[ [ "Rahman", "Muhammad", "" ], [ "Figliolini", "Sachi", "" ], [ "Kim", "Joyce", "" ], [ "Cedeno", "Eivy", "" ], [ "Kleier", "Charles", "" ], [ "Shah", "Chirag", "" ], [ "Chadha", "Aman", "" ] ]
2308.14363
Jinliang Yuan
Jinliang Yuan, Chen Yang, Dongqi Cai, Shihe Wang, Xin Yuan, Zeling Zhang, Xiang Li, Dingge Zhang, Hanzi Mei, Xianqing Jia, Shangguang Wang, Mengwei Xu
Mobile Foundation Model as Firmware
17 pages, 15 figures, published to ACM MobiCom'24
The 30th Annual International Conference on Mobile Computing and Networking, 2024
10.1145/3636534.3649361
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep learning models aimed at local execution. A key realization driving this work is the notable fragmentation among these models, characterized by varied architectures, operators, and implementations. This fragmentation imposes a significant burden on the comprehensive optimization of hardware, system settings, and algorithms. Buoyed by the recent strides in large foundation models, this work introduces a pioneering paradigm for mobile AI: a collaborative management approach between the mobile OS and hardware, overseeing a foundational model capable of serving a broad spectrum of mobile AI tasks, if not all. This foundational model resides within the NPU and remains impervious to app or OS revisions, akin to firmware. Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks. From this concept emerges a concrete instantiation known as \sys. It amalgamates a curated selection of publicly available Large Language Models (LLMs) and facilitates dynamic data flow. This concept's viability is substantiated through the creation of an exhaustive benchmark encompassing 38 mobile AI tasks spanning 50 datasets, including domains such as Computer Vision (CV), Natural Language Processing (NLP), audio, sensing, and multimodal inputs. Spanning this benchmark, \sys unveils its impressive performance. It attains accuracy parity in 85\% of tasks, demonstrates improved scalability in terms of storage and memory, and offers satisfactory inference speed on Commercial Off-The-Shelf (COTS) mobile devices fortified with NPU support. This stands in stark contrast to task-specific models tailored for individual applications.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 07:21:26 GMT" }, { "version": "v2", "created": "Mon, 11 Mar 2024 16:18:17 GMT" }, { "version": "v3", "created": "Tue, 12 Mar 2024 02:17:03 GMT" } ]
1,710,288,000,000
[ [ "Yuan", "Jinliang", "" ], [ "Yang", "Chen", "" ], [ "Cai", "Dongqi", "" ], [ "Wang", "Shihe", "" ], [ "Yuan", "Xin", "" ], [ "Zhang", "Zeling", "" ], [ "Li", "Xiang", "" ], [ "Zhang", "Dingge", "" ], [ "Mei", "Hanzi", "" ], [ "Jia", "Xianqing", "" ], [ "Wang", "Shangguang", "" ], [ "Xu", "Mengwei", "" ] ]
2308.14390
Konstantinos Lampropoulos
Konstantinos Lampropoulos, Thanos Kosmidis, Serge Autexier, Milos Savic, Manos Athanatos, Miltiadis Kokkonidis, Tzortzia Koutsouri, Anamaria Vizitiu, Antonios Valachis, Miriam Quintero Padron
ASCAPE: An open AI ecosystem to support the quality of life of cancer patients
null
null
10.1109/ICHI52183.2021.00054
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The latest cancer statistics indicate a decrease in cancer-related mortality. However, due to the growing and ageing population, the absolute number of people living with cancer is set to keep increasing. This paper presents ASCAPE, an open AI infrastructure that takes advantage of the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support cancer patients quality of life (QoL). With ASCAPE health stakeholders (e.g. hospitals) can locally process their private medical data and then share the produced knowledge (ML models) through the open AI infrastructure.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 08:14:12 GMT" } ]
1,693,267,200,000
[ [ "Lampropoulos", "Konstantinos", "" ], [ "Kosmidis", "Thanos", "" ], [ "Autexier", "Serge", "" ], [ "Savic", "Milos", "" ], [ "Athanatos", "Manos", "" ], [ "Kokkonidis", "Miltiadis", "" ], [ "Koutsouri", "Tzortzia", "" ], [ "Vizitiu", "Anamaria", "" ], [ "Valachis", "Antonios", "" ], [ "Padron", "Miriam Quintero", "" ] ]
2308.14474
Shuxian Du
Shuxian Du, Yaxiu Sun and Changyi Du
Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI
7 pages
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In the current ML field models are getting larger and more complex, and data used for model training are also getting larger in quantity and higher in dimensions. Therefore, in order to train better models, and save training time and computational resources, a good Feature Selection (FS) method in the preprocessing stage is necessary. Feature importance (FI) is of great importance since it is the basis of feature selection. Therefore, this paper creatively introduces the calculation of PN (the probability of Necessity), PN (the probability of Sufficiency), and PNS (the probability of Necessity and Sufficiency) of Causality into quantifying feature importance and creates 3 new FI measuring methods, PN-FI, which means how much importance a feature has in image recognition tasks, PS-FI that means how much importance a feature has in image generating tasks, and PNS-FI which measures both. The main body of this paper is three RCTs, with whose results we show how PS-FI, PN-FI, and PNS-FI of 3 features, dog nose, dog eyes, and dog mouth are calculated. The experiments show that firstly, FI values are intervals with tight upper and lower bounds. Secondly, the feature dog eyes has the most importance while the other two have almost the same. Thirdly, the bounds of PNS and PN are tighter than the bounds of PS.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 10:24:51 GMT" }, { "version": "v2", "created": "Mon, 18 Sep 2023 06:28:41 GMT" } ]
1,695,081,600,000
[ [ "Du", "Shuxian", "" ], [ "Sun", "Yaxiu", "" ], [ "Du", "Changyi", "" ] ]
2308.14475
Mozhgan Vazifehdoostirani
Mozhgan Vazifehdoostirani, Laura Genga, Xixi Lu, Rob Verhoeven, Hanneke van Laarhoven, Remco Dijkman
Interactive Multi Interest Process Pattern Discovery
16 pages, 5 figures, To appear in the preceedings of 21st International Conference on Business Process Management (BPM), 11-15 September 2023, Utrecht, the Netherlands
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi interest driven framework for process pattern discovery aimed at identifying patterns that are optimal according to a multi-dimensional analysis goal. The proposed approach is iterative and interactive, thus taking experts knowledge into account during the discovery process. The paper focuses on a concrete analysis goal, i.e., deriving process patterns that affect the process outcome. We evaluate the approach on real world event logs in both interactive and fully automated settings. The approach extracted meaningful patterns validated by expert knowledge in the interactive setting. Patterns extracted in the automated settings consistently led to prediction performance comparable to or better than patterns derived considering single interest dimensions without requiring user defined thresholds.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 10:26:37 GMT" } ]
1,693,267,200,000
[ [ "Vazifehdoostirani", "Mozhgan", "" ], [ "Genga", "Laura", "" ], [ "Lu", "Xixi", "" ], [ "Verhoeven", "Rob", "" ], [ "van Laarhoven", "Hanneke", "" ], [ "Dijkman", "Remco", "" ] ]
2308.14550
Aizaz Sharif
Aizaz Sharif and Dusica Marijan
ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Autonomous vehicles are advanced driving systems that are well known to be vulnerable to various adversarial attacks, compromising vehicle safety and posing a risk to other road users. Rather than actively training complex adversaries by interacting with the environment, there is a need to first intelligently find and reduce the search space to only those states where autonomous vehicles are found to be less confident. In this paper, we propose a black-box testing framework ReMAV that uses offline trajectories first to analyze the existing behavior of autonomous vehicles and determine appropriate thresholds to find the probability of failure events. To this end, we introduce a three-step methodology which i) uses offline state action pairs of any autonomous vehicle under test, ii) builds an abstract behavior representation using our designed reward modeling technique to analyze states with uncertain driving decisions, and iii) uses a disturbance model for minimal perturbation attacks where the driving decisions are less confident. Our reward modeling technique helps in creating a behavior representation that allows us to highlight regions of likely uncertain behavior even when the standard autonomous vehicle performs well. We perform our experiments in a high-fidelity urban driving environment using three different driving scenarios containing single- and multi-agent interactions. Our experiment shows an increase in 35, 23, 48, and 50% in the occurrences of vehicle collision, road object collision, pedestrian collision, and offroad steering events, respectively by the autonomous vehicle under test, demonstrating a significant increase in failure events. We compare ReMAV with two baselines and show that ReMAV demonstrates significantly better effectiveness in generating failure events compared to the baselines in all evaluation metrics.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 13:09:00 GMT" }, { "version": "v2", "created": "Sat, 30 Dec 2023 11:05:53 GMT" } ]
1,704,153,600,000
[ [ "Sharif", "Aizaz", "" ], [ "Marijan", "Dusica", "" ] ]
2308.14719
Gal Elgavish
Gal Elgavish
Hierarchical Time Series Forecasting with Bayesian Modeling
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We encounter time series data in many domains such as finance, physics, business, and weather. One of the main tasks of time series analysis, one that helps to take informed decisions under uncertainty, is forecasting. Time series are often hierarchically structured, e.g., a company sales might be broken down into different regions, and each region into different stores. In some cases the number of series in the hierarchy is too big to fit in a single model to produce forecasts in relevant time, and a decentralized approach is beneficial. One way to do this is to train independent forecasting models for each series and for some summary statistics series implied by the hierarchy (e.g. the sum of all series) and to pass those models to a reconciliation algorithm to improve those forecasts by sharing information between the series. In this work we focus on the reconciliation step, and propose a method to do so from a Bayesian perspective - Bayesian forecast reconciliation. We also define the common case of linear Gaussian reconciliation, where the forecasts are Gaussian and the hierarchy has linear structure, and show that we can compute reconciliation in closed form. We evaluate these methods on synthetic and real data sets, and compare them to other work in this field.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 17:20:47 GMT" } ]
1,693,267,200,000
[ [ "Elgavish", "Gal", "" ] ]
2308.14732
Renato Krohling
Renato A. Krohling
Bayesian artificial brain with ChatGPT
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
This paper aims to investigate the mathematical problem-solving capabilities of Chat Generative Pre-Trained Transformer (ChatGPT) in case of Bayesian reasoning. The study draws inspiration from Zhu & Gigerenzer's research in 2006, which posed the question: Can children reason the Bayesian way? In the pursuit of answering this question, a set of 10 Bayesian reasoning problems were presented. The results of their work revealed that children's ability to reason effectively using Bayesian principles is contingent upon a well-structured information representation. In this paper, we present the same set of 10 Bayesian reasoning problems to ChatGPT. Remarkably, the results demonstrate that ChatGPT provides the right solutions to all problems.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 17:34:24 GMT" } ]
1,693,267,200,000
[ [ "Krohling", "Renato A.", "" ] ]
2308.14840
Mihai Christodorescu
Clark Barrett, Brad Boyd, Elie Burzstein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, Kathleen Fisher, Tatsunori Hashimoto, Dan Hendrycks, Somesh Jha, Daniel Kang, Florian Kerschbaum, Eric Mitchell, John Mitchell, Zulfikar Ramzan, Khawaja Shams, Dawn Song, Ankur Taly, Diyi Yang
Identifying and Mitigating the Security Risks of Generative AI
null
Foundations and Trends in Privacy and Security 6 (2023) 1-52
10.1561/3300000041
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Every major technical invention resurfaces the dual-use dilemma -- the new technology has the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities (e.g., in-context learning, code-completion, and text-to-image generation and editing). However, GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. This paper is not meant to be comprehensive, but is rather an attempt to synthesize some of the interesting findings from the workshop. We discuss short-term and long-term goals for the community on this topic. We hope this paper provides both a launching point for a discussion on this important topic as well as interesting problems that the research community can work to address.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 18:51:09 GMT" }, { "version": "v2", "created": "Sun, 15 Oct 2023 05:05:12 GMT" }, { "version": "v3", "created": "Tue, 17 Oct 2023 23:27:11 GMT" }, { "version": "v4", "created": "Fri, 29 Dec 2023 00:30:34 GMT" } ]
1,704,067,200,000
[ [ "Barrett", "Clark", "" ], [ "Boyd", "Brad", "" ], [ "Burzstein", "Elie", "" ], [ "Carlini", "Nicholas", "" ], [ "Chen", "Brad", "" ], [ "Choi", "Jihye", "" ], [ "Chowdhury", "Amrita Roy", "" ], [ "Christodorescu", "Mihai", "" ], [ "Datta", "Anupam", "" ], [ "Feizi", "Soheil", "" ], [ "Fisher", "Kathleen", "" ], [ "Hashimoto", "Tatsunori", "" ], [ "Hendrycks", "Dan", "" ], [ "Jha", "Somesh", "" ], [ "Kang", "Daniel", "" ], [ "Kerschbaum", "Florian", "" ], [ "Mitchell", "Eric", "" ], [ "Mitchell", "John", "" ], [ "Ramzan", "Zulfikar", "" ], [ "Shams", "Khawaja", "" ], [ "Song", "Dawn", "" ], [ "Taly", "Ankur", "" ], [ "Yang", "Diyi", "" ] ]
2308.15002
Yi Xu
Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu, Lei Zhou, Xinbing Wang, Chenghu Zhou
Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation
Extended version of AAAI paper arXiv:2211.10904
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Temporal knowledge graphs, representing the dynamic relationships and interactions between entities over time, have been identified as a promising approach for event forecasting. However, a limitation of most temporal knowledge graph reasoning methods is their heavy reliance on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current state of affairs is often the result of a combination of historical information and underlying factors that are not directly observable. To this end, we investigate the limits of historical information for temporal knowledge graph extrapolation and propose a new event forecasting model called Contrastive Event Network (CENET) based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that best match the given query. Simultaneously, by launching contrastive learning, it trains representations of queries to probe whether the current moment is more dependent on historical or non-historical events. These representations further help train a binary classifier, whose output is a boolean mask, indicating the related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 03:26:38 GMT" } ]
1,693,353,600,000
[ [ "Xu", "Yi", "" ], [ "Ou", "Junjie", "" ], [ "Xu", "Hui", "" ], [ "Fu", "Luoyi", "" ], [ "Zhou", "Lei", "" ], [ "Wang", "Xinbing", "" ], [ "Zhou", "Chenghu", "" ] ]
2308.15030
Rui Kong
Rui Kong, Yuanchun Li, Qingtian Feng, Weijun Wang, Xiaozhou Ye, Ye Ouyang, Linghe Kong, Yunxin Liu
SwapMoE: Serving Off-the-shelf MoE-based Large Language Models with Tunable Memory Budget
Accepted at ACL 2024
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) with conditionally-activated parallel experts. However, serving MoE models on memory-constrained devices is challenging due to the large parameter size. Typical solutions such as memory swapping or expert pruning may lead to significantly higher latency or severe accuracy loss. In this paper, we introduce SwapMoE, a framework for efficient serving of MoE-based large language models with tunable memory budgets. The main idea of SwapMoE is to keep a small dynamic set of important experts, namely Virtual Experts, in the main memory for inference, while seamlessly maintaining how the Virtual Experts map to the actual experts. Experiments have shown that SwapMoE can reduce the memory footprint while maintaining reasonable accuracy. For example, on text summarization tasks with Switch Transformer, SwapMoE can reduce the memory consumption from 14.2 GiB to 4.7 GiB, together with 50\% latency reduction and a slight Rouge-2 score drop of 0.041.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 05:25:21 GMT" }, { "version": "v2", "created": "Thu, 28 Dec 2023 02:53:41 GMT" }, { "version": "v3", "created": "Tue, 28 May 2024 02:08:30 GMT" }, { "version": "v4", "created": "Wed, 29 May 2024 08:25:03 GMT" } ]
1,717,027,200,000
[ [ "Kong", "Rui", "" ], [ "Li", "Yuanchun", "" ], [ "Feng", "Qingtian", "" ], [ "Wang", "Weijun", "" ], [ "Ye", "Xiaozhou", "" ], [ "Ouyang", "Ye", "" ], [ "Kong", "Linghe", "" ], [ "Liu", "Yunxin", "" ] ]
2308.15168
Erkan Karabulut
Erkan Karabulut, Salvatore F. Pileggi, Paul Groth and Victoria Degeler
Ontologies in Digital Twins: A Systematic Literature Review
The Systematic Literature Review (SLR) is submitted to Future Generation Computer System journal's Special Issue on Digital Twin for Future Networks and Emerging IoT Applications (2023)
null
10.1016/j.future.2023.12.013
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Digital Twins (DT) facilitate monitoring and reasoning processes in cyber-physical systems. They have progressively gained popularity over the past years because of intense research activity and industrial advancements. Cognitive Twins is a novel concept, recently coined to refer to the involvement of Semantic Web technology in DTs. Recent studies address the relevance of ontologies and knowledge graphs in the context of DTs, in terms of knowledge representation, interoperability and automatic reasoning. However, there is no comprehensive analysis of how semantic technologies, and specifically ontologies, are utilized within DTs. This Systematic Literature Review (SLR) is based on the analysis of 82 research articles, that either propose or benefit from ontologies with respect to DT. The paper uses different analysis perspectives, including a structural analysis based on a reference DT architecture, and an application-specific analysis to specifically address the different domains, such as Manufacturing and Infrastructure. The review also identifies open issues and possible research directions on the usage of ontologies and knowledge graphs in DTs.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 09:52:21 GMT" } ]
1,703,203,200,000
[ [ "Karabulut", "Erkan", "" ], [ "Pileggi", "Salvatore F.", "" ], [ "Groth", "Paul", "" ], [ "Degeler", "Victoria", "" ] ]
2308.15239
Filipe Assun\c{c}\~ao
Sofia Aparicio, Samuel Arcadinho, Jo\~ao Nadkarni, David Apar\'icio, Jo\~ao Lages, Mariana Louren\c{c}o, Bart{\l}omiej Matejczyk, Filipe Assun\c{c}\~ao
Natural language to SQL in low-code platforms
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
One of the developers' biggest challenges in low-code platforms is retrieving data from a database using SQL queries. Here, we propose a pipeline allowing developers to write natural language (NL) to retrieve data. In this study, we collect, label, and validate data covering the SQL queries most often performed by OutSystems users. We use that data to train a NL model that generates SQL. Alongside this, we describe the entire pipeline, which comprises a feedback loop that allows us to quickly collect production data and use it to retrain our SQL generation model. Using crowd-sourcing, we collect 26k NL and SQL pairs and obtain an additional 1k pairs from production data. Finally, we develop a UI that allows developers to input a NL query in a prompt and receive a user-friendly representation of the resulting SQL query. We use A/B testing to compare four different models in production and observe a 240% improvement in terms of adoption of the feature, 220% in terms of engagement rate, and a 90% decrease in failure rate when compared against the first model that we put into production, showcasing the effectiveness of our pipeline in continuously improving our feature.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 11:59:02 GMT" } ]
1,693,353,600,000
[ [ "Aparicio", "Sofia", "" ], [ "Arcadinho", "Samuel", "" ], [ "Nadkarni", "João", "" ], [ "Aparício", "David", "" ], [ "Lages", "João", "" ], [ "Lourenço", "Mariana", "" ], [ "Matejczyk", "Bartłomiej", "" ], [ "Assunção", "Filipe", "" ] ]
2308.15324
Pengwei Xing
Pengwei Xing, Songtao Lu, Han Yu
Federated Neuro-Symbolic Learning
accepted by ICML 2024
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Neuro-symbolic learning (NSL) models complex symbolic rule patterns into latent variable distributions by neural networks, which reduces rule search space and generates unseen rules to improve downstream task performance. Centralized NSL learning involves directly acquiring data from downstream tasks, which is not feasible for federated learning (FL). To address this limitation, we shift the focus from such a one-to-one interactive neuro-symbolic paradigm to one-to-many Federated Neuro-Symbolic Learning framework (FedNSL) with latent variables as the FL communication medium. Built on the basis of our novel reformulation of the NSL theory, FedNSL is capable of identifying and addressing rule distribution heterogeneity through a simple and effective Kullback-Leibler (KL) divergence constraint on rule distribution applicable under the FL setting. It further theoretically adjusts variational expectation maximization (V-EM) to reduce the rule search space across domains. This is the first incorporation of distribution-coupled bilevel optimization into FL. Extensive experiments based on both synthetic and real-world data demonstrate significant advantages of FedNSL compared to five state-of-the-art methods. It outperforms the best baseline by 17% and 29% in terms of unbalanced average training accuracy and unseen average testing accuracy, respectively.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 14:20:17 GMT" }, { "version": "v2", "created": "Mon, 27 May 2024 14:29:29 GMT" } ]
1,716,854,400,000
[ [ "Xing", "Pengwei", "" ], [ "Lu", "Songtao", "" ], [ "Yu", "Han", "" ] ]
2308.15339
Elham Nasarian
Elham Nasarian, Danial Sharifrazi, Saman Mohsenirad, Kwok Tsui, Roohallah Alizadehsani
AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others. In this context, artificial intelligence (AI) can help clinicians identify high-risk patients early in the diagnostic process, by synthesizing information from multiple factors. To this aim, Machine Learning algorithms are used to classify patients based on their CAD disease risk. In this study, we contribute to this research filed by developing a methodology for balancing and augmenting data for more accurate prediction when the data is imbalanced and the sample size is small. The methodology can be used in a variety of other situations, particularly when data collection is expensive and the sample size is small. The experimental results revealed that the average accuracy of our proposed method for CAD prediction was 95.36, and was higher than random forest (RF), decision tree (DT), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN).
[ { "version": "v1", "created": "Tue, 29 Aug 2023 14:33:38 GMT" } ]
1,693,353,600,000
[ [ "Nasarian", "Elham", "" ], [ "Sharifrazi", "Danial", "" ], [ "Mohsenirad", "Saman", "" ], [ "Tsui", "Kwok", "" ], [ "Alizadehsani", "Roohallah", "" ] ]
2308.15390
Leila Bagheriye
Otto van der Himst, Leila Bagheriye, and Johan Kwisthout
Bayesian Integration of Information Using Top-Down Modulated WTA Networks
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Winner Take All (WTA) circuits a type of Spiking Neural Networks (SNN) have been suggested as facilitating the brain's ability to process information in a Bayesian manner. Research has shown that WTA circuits are capable of approximating hierarchical Bayesian models via Expectation Maximization (EM). So far, research in this direction has focused on bottom up processes. This is contrary to neuroscientific evidence that shows that, besides bottom up processes, top down processes too play a key role in information processing by the human brain. Several functions ascribed to top down processes include direction of attention, adjusting for expectations, facilitation of encoding and recall of learned information, and imagery. This paper explores whether WTA circuits are suitable for further integrating information represented in separate WTA networks. Furthermore, it explores whether, and under what circumstances, top down processes can improve WTA network performance with respect to inference and learning. The results show that WTA circuits are capable of integrating the probabilistic information represented by other WTA networks, and that top down processes can improve a WTA network's inference and learning performance. Notably, it is able to do this according to key neuromorphic principles, making it ideal for low-latency and energy efficient implementation on neuromorphic hardware.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 15:33:51 GMT" } ]
1,693,353,600,000
[ [ "van der Himst", "Otto", "" ], [ "Bagheriye", "Leila", "" ], [ "Kwisthout", "Johan", "" ] ]
2308.15514
Robert Trager
Robert Trager, Ben Harack, Anka Reuel, Allison Carnegie, Lennart Heim, Lewis Ho, Sarah Kreps, Ranjit Lall, Owen Larter, Se\'an \'O h\'Eigeartaigh, Simon Staffell, Jos\'e Jaime Villalobos
International Governance of Civilian AI: A Jurisdictional Certification Approach
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
This report describes trade-offs in the design of international governance arrangements for civilian artificial intelligence (AI) and presents one approach in detail. This approach represents the extension of a standards, licensing, and liability regime to the global level. We propose that states establish an International AI Organization (IAIO) to certify state jurisdictions (not firms or AI projects) for compliance with international oversight standards. States can give force to these international standards by adopting regulations prohibiting the import of goods whose supply chains embody AI from non-IAIO-certified jurisdictions. This borrows attributes from models of existing international organizations, such as the International Civilian Aviation Organization (ICAO), the International Maritime Organization (IMO), and the Financial Action Task Force (FATF). States can also adopt multilateral controls on the export of AI product inputs, such as specialized hardware, to non-certified jurisdictions. Indeed, both the import and export standards could be required for certification. As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 16:43:59 GMT" }, { "version": "v2", "created": "Mon, 11 Sep 2023 14:03:37 GMT" } ]
1,694,476,800,000
[ [ "Trager", "Robert", "" ], [ "Harack", "Ben", "" ], [ "Reuel", "Anka", "" ], [ "Carnegie", "Allison", "" ], [ "Heim", "Lennart", "" ], [ "Ho", "Lewis", "" ], [ "Kreps", "Sarah", "" ], [ "Lall", "Ranjit", "" ], [ "Larter", "Owen", "" ], [ "hÉigeartaigh", "Seán Ó", "" ], [ "Staffell", "Simon", "" ], [ "Villalobos", "José Jaime", "" ] ]
2308.15568
Singh Akansha
Singh Akansha
Over-Squashing in Graph Neural Networks: A Comprehensive survey
14 pages
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Graph Neural Networks (GNNs) revolutionize machine learning for graph-structured data, effectively capturing complex relationships. They disseminate information through interconnected nodes, but long-range interactions face challenges known as "over-squashing". This survey delves into the challenge of over-squashing in Graph Neural Networks (GNNs), where long-range information dissemination is hindered, impacting tasks reliant on intricate long-distance interactions. It comprehensively explores the causes, consequences, and mitigation strategies for over-squashing. Various methodologies are reviewed, including graph rewiring, novel normalization, spectral analysis, and curvature-based strategies, with a focus on their trade-offs and effectiveness. The survey also discusses the interplay between over-squashing and other GNN limitations, such as over-smoothing, and provides a taxonomy of models designed to address these issues in node and graph-level tasks. Benchmark datasets for performance evaluation are also detailed, making this survey a valuable resource for researchers and practitioners in the GNN field.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 18:46:15 GMT" }, { "version": "v2", "created": "Mon, 4 Sep 2023 11:54:33 GMT" }, { "version": "v3", "created": "Sun, 17 Sep 2023 13:06:01 GMT" }, { "version": "v4", "created": "Sat, 21 Oct 2023 09:39:48 GMT" }, { "version": "v5", "created": "Tue, 28 Nov 2023 11:03:06 GMT" }, { "version": "v6", "created": "Mon, 29 Apr 2024 14:15:42 GMT" } ]
1,714,435,200,000
[ [ "Akansha", "Singh", "" ] ]
2308.15620
Pakizar Shamoi Dr
Izbassar Assylzhan, Muragul Muratbekova, Daniyar Amangeldi, Nazzere Oryngozha, Anna Ogorodova, Pakizar Shamoi
Intelligent System for Assessing University Student Personality Development and Career Readiness
8 pages. Submitted to Elsevier conference
null
10.1016/j.procs.2023.12.138
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
While academic metrics such as transcripts and GPA are commonly used to evaluate students' knowledge acquisition, there is a lack of comprehensive metrics to measure their preparedness for the challenges of post-graduation life. This research paper explores the impact of various factors on university students' readiness for change and transition, with a focus on their preparedness for careers. The methodology employed in this study involves designing a survey based on Paul J. Mayer's "The Balance Wheel" to capture students' sentiments on various life aspects, including satisfaction with the educational process and expectations of salary. The collected data from a KBTU student survey (n=47) were processed through machine learning models: Linear Regression, Support Vector Regression (SVR), Random Forest Regression. Subsequently, an intelligent system was built using these models and fuzzy sets. The system is capable of evaluating graduates' readiness for their future careers and demonstrates a high predictive power. The findings of this research have practical implications for educational institutions. Such an intelligent system can serve as a valuable tool for universities to assess and enhance students' preparedness for post-graduation challenges. By recognizing the factors contributing to students' readiness for change, universities can refine curricula and processes to better prepare students for their career journeys.
[ { "version": "v1", "created": "Tue, 29 Aug 2023 20:32:58 GMT" } ]
1,705,881,600,000
[ [ "Assylzhan", "Izbassar", "" ], [ "Muratbekova", "Muragul", "" ], [ "Amangeldi", "Daniyar", "" ], [ "Oryngozha", "Nazzere", "" ], [ "Ogorodova", "Anna", "" ], [ "Shamoi", "Pakizar", "" ] ]
2308.15802
Junjie Zhang
Yangkun Chen, Joseph Suarez, Junjie Zhang, Chenghui Yu, Bo Wu, Hanmo Chen, Hengman Zhu, Rui Du, Shanliang Qian, Shuai Liu, Weijun Hong, Jinke He, Yibing Zhang, Liang Zhao, Clare Zhu, Julian Togelius, Sharada Mohanty, Jiaxin Chen, Xiu Li, Xiaolong Zhu, Phillip Isola
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-agent systems: participants train teams of agents to complete a multi-task objective against opponents not seen during training. The competition combines relatively complex environment design with large numbers of agents in the environment. The top submissions demonstrate strong success on this task using mostly standard reinforcement learning (RL) methods combined with domain-specific engineering. We summarize the competition design and results and suggest that, as an academic community, competitions may be a powerful approach to solving hard problems and establishing a solid benchmark for algorithms. We will open-source our benchmark including the environment wrapper, baselines, a visualization tool, and selected policies for further research.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 07:16:11 GMT" } ]
1,693,440,000,000
[ [ "Chen", "Yangkun", "" ], [ "Suarez", "Joseph", "" ], [ "Zhang", "Junjie", "" ], [ "Yu", "Chenghui", "" ], [ "Wu", "Bo", "" ], [ "Chen", "Hanmo", "" ], [ "Zhu", "Hengman", "" ], [ "Du", "Rui", "" ], [ "Qian", "Shanliang", "" ], [ "Liu", "Shuai", "" ], [ "Hong", "Weijun", "" ], [ "He", "Jinke", "" ], [ "Zhang", "Yibing", "" ], [ "Zhao", "Liang", "" ], [ "Zhu", "Clare", "" ], [ "Togelius", "Julian", "" ], [ "Mohanty", "Sharada", "" ], [ "Chen", "Jiaxin", "" ], [ "Li", "Xiu", "" ], [ "Zhu", "Xiaolong", "" ], [ "Isola", "Phillip", "" ] ]
2308.15819
Tuukka Korhonen
Tuukka Korhonen, Matti J\"arvisalo
SharpSAT-TD in Model Counting Competitions 2021-2023
3 pages
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We describe SharpSAT-TD, our submission to the unweighted and weighted tracks of the Model Counting Competition in 2021-2023, which has won in total $6$ first places in different tracks of the competition. SharpSAT-TD is based on SharpSAT [Thurley, SAT 2006], with the primary novel modification being the use of tree decompositions in the variable selection heuristic as introduced by the authors in [CP 2021]. Unlike the version of SharpSAT-TD evaluated in [CP 2021], the current version that is available in https://github.com/Laakeri/sharpsat-td features also other significant modifications compared to the original SharpSAT, for example, a new preprocessor.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 07:43:12 GMT" } ]
1,693,440,000,000
[ [ "Korhonen", "Tuukka", "" ], [ "Järvisalo", "Matti", "" ] ]