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2308.15833
Zhen Zhang
Zhen Zhang and Hongrui Sun and Hui Sun
Depth analysis of battery performance based on a data-driven approach
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Capacity attenuation is one of the most intractable issues in the current of application of the cells. The disintegration mechanism is well known to be very complex across the system. It is a great challenge to fully comprehend this process and predict the process accurately. Thus, the machine learning (ML) technology is employed to predict the specific capacity change of the cell throughout the cycle and grasp this intricate procedure. Different from the previous work, according to the WOA-ELM model proposed in this work (R2 = 0.9999871), the key factors affecting the specific capacity of the battery are determined, and the defects in the machine learning black box are overcome by the interpretable model. Their connection with the structural damage of electrode materials and battery failure during battery cycling is comprehensively explained, revealing their essentiality to battery performance, which is conducive to superior research on contemporary batteries and modification.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 08:15:27 GMT" } ]
1,693,440,000,000
[ [ "Zhang", "Zhen", "" ], [ "Sun", "Hongrui", "" ], [ "Sun", "Hui", "" ] ]
2308.15863
EPTCS
Richard Comploi-Taupe (Siemens AG \"Osterreich, Vienna, Austria)
Inductive Learning of Declarative Domain-Specific Heuristics for ASP
In Proceedings ICLP 2023, arXiv:2308.14898
EPTCS 385, 2023, pp. 129-140
10.4204/EPTCS.385.14
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Domain-specific heuristics are a crucial technique for the efficient solving of problems that are large or computationally hard. Answer Set Programming (ASP) systems support declarative specifications of domain-specific heuristics to improve solving performance. However, such heuristics must be invented manually so far. Inventing domain-specific heuristics for answer-set programs requires expertise with the domain under consideration and familiarity with ASP syntax, semantics, and solving technology. The process of inventing useful heuristics would highly profit from automatic support. This paper presents a novel approach to the automatic learning of such heuristics. We use Inductive Logic Programming (ILP) to learn declarative domain-specific heuristics from examples stemming from (near-)optimal answer sets of small but representative problem instances. Our experimental results indicate that the learned heuristics can improve solving performance and solution quality when solving larger, harder instances of the same problem.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 08:55:17 GMT" } ]
1,693,440,000,000
[ [ "Comploi-Taupe", "Richard", "", "Siemens AG Österreich, Vienna, Austria" ] ]
2308.15879
EPTCS
Mario Alviano (University of Calabria), Ly Ly Trieu (New Mexico State Universty), Tran Cao Son (New Mexico State Universty), Marcello Balduccini (Saint Joseph's University)
Explanations for Answer Set Programming
In Proceedings ICLP 2023, arXiv:2308.14898
EPTCS 385, 2023, pp. 27-40
10.4204/EPTCS.385.4
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The paper presents an enhancement of xASP, a system that generates explanation graphs for Answer Set Programming (ASP). Different from xASP, the new system, xASP2, supports different clingo constructs like the choice rules, the constraints, and the aggregates such as #sum, #min. This work formalizes and presents an explainable artificial intelligence system for a broad fragment of ASP, capable of shrinking as much as possible the set of assumptions and presenting explanations in terms of directed acyclic graphs.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 09:03:07 GMT" } ]
1,693,440,000,000
[ [ "Alviano", "Mario", "", "University of Calabria" ], [ "Trieu", "Ly Ly", "", "New Mexico State\n Universty" ], [ "Son", "Tran Cao", "", "New Mexico State Universty" ], [ "Balduccini", "Marcello", "", "Saint Joseph's University" ] ]
2308.15891
EPTCS
Francesca Toni (Department of Computing, Imperial College London, UK), Nico Potyka (Department of Computing, Imperial College London, UK), Markus Ulbricht (Department of Computer Science, Leipzig University, Germany), Pietro Totis (Department of Computer Science, KU Leuven, Belgium)
Understanding ProbLog as Probabilistic Argumentation
In Proceedings ICLP 2023, arXiv:2308.14898
EPTCS 385, 2023, pp. 183-189
10.4204/EPTCS.385.18
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper we study connections between ProbLog and a variant of another well-known formalism combining symbolic reasoning and reasoning under uncertainty, i.e. probabilistic argumentation. Specifically, we show that ProbLog is an instance of a form of Probabilistic Abstract Argumentation (PAA) that builds upon Assumption-Based Argumentation (ABA). The connections pave the way towards equipping ProbLog with alternative semantics, inherited from PAA/PABA, as well as obtaining novel argumentation semantics for PAA/PABA, leveraging on prior connections between ProbLog and argumentation. Further, the connections pave the way towards novel forms of argumentative explanations for ProbLog's outputs.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 09:05:32 GMT" } ]
1,693,440,000,000
[ [ "Toni", "Francesca", "", "Department of Computing, Imperial College London, UK" ], [ "Potyka", "Nico", "", "Department of Computing, Imperial College London, UK" ], [ "Ulbricht", "Markus", "", "Department of Computer Science, Leipzig University, Germany" ], [ "Totis", "Pietro", "", "Department of Computer Science, KU Leuven, Belgium" ] ]
2308.15898
EPTCS
Alessandro Dal Pal\`u (Universit\`a di Parma, Italy), Agostino Dovier (Universit\`a di Udine, Italy), Andrea Formisano (Universit\`a di Udine, Italy)
An xAI Approach for Data-to-Text Processing with ASP
In Proceedings ICLP 2023, arXiv:2308.14898
EPTCS 385, 2023, pp. 353-366
10.4204/EPTCS.385.38
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The generation of natural language text from data series gained renewed interest among AI research goals. Not surprisingly, the few proposals in the state of the art are based on training some system, in order to produce a text that describes and that is coherent to the data provided as input. Main challenges of such approaches are the proper identification of "what" to say (the key descriptive elements to be addressed in the data) and "how" to say: the correspondence and accuracy between data and text, the presence of contradictions/redundancy in the text, the control of the amount of synthesis. This paper presents a framework that is compliant with xAI requirements. In particular we model ASP/Python programs that enable an explicit control of accuracy errors and amount of synthesis, with proven optimal solutions. The text description is hierarchically organized, in a top-down structure where text is enriched with further details, according to logic rules. The generation of natural language descriptions' structure is also managed by logic rules.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 09:09:09 GMT" } ]
1,693,440,000,000
[ [ "Palù", "Alessandro Dal", "", "Università di Parma, Italy" ], [ "Dovier", "Agostino", "", "Università di Udine, Italy" ], [ "Formisano", "Andrea", "", "Università di Udine,\n Italy" ] ]
2308.15926
Jianghong Ma
Dezhao Yang, Jianghong Ma, Shanshan Feng, Haijun Zhang, Zhao Zhang
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In the information age, recommendation systems are vital for efficiently filtering information and identifying user preferences. Online social platforms have enriched these systems by providing valuable auxiliary information. Socially connected users are assumed to share similar preferences, enhancing recommendation accuracy and addressing cold start issues. However, empirical findings challenge the assumption, revealing that certain social connections can actually harm system performance. Our statistical analysis indicates a significant amount of noise in the social network, where many socially connected users do not share common interests. To address this issue, we propose an innovative \underline{I}nterest-aware \underline{D}enoising and \underline{V}iew-guided \underline{T}uning (IDVT) method for the social recommendation. The first ID part effectively denoises social connections. Specifically, the denoising process considers both social network structure and user interaction interests in a global view. Moreover, in this global view, we also integrate denoised social information (social domain) into the propagation of the user-item interactions (collaborative domain) and aggregate user representations from two domains using a gating mechanism. To tackle potential user interest loss and enhance model robustness within the global view, our second VT part introduces two additional views (local view and dropout-enhanced view) for fine-tuning user representations in the global view through contrastive learning. Extensive evaluations on real-world datasets with varying noise ratios demonstrate the superiority of IDVT over state-of-the-art social recommendation methods.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 10:03:55 GMT" } ]
1,693,440,000,000
[ [ "Yang", "Dezhao", "" ], [ "Ma", "Jianghong", "" ], [ "Feng", "Shanshan", "" ], [ "Zhang", "Haijun", "" ], [ "Zhang", "Zhao", "" ] ]
2308.15969
Jasmina Gajcin
Jasmina Gajcin, James McCarthy, Rahul Nair, Radu Marinescu, Elizabeth Daly, Ivana Dusparic
Iterative Reward Shaping using Human Feedback for Correcting Reward Misspecification
7 pages, 2 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
A well-defined reward function is crucial for successful training of an reinforcement learning (RL) agent. However, defining a suitable reward function is a notoriously challenging task, especially in complex, multi-objective environments. Developers often have to resort to starting with an initial, potentially misspecified reward function, and iteratively adjusting its parameters, based on observed learned behavior. In this work, we aim to automate this process by proposing ITERS, an iterative reward shaping approach using human feedback for mitigating the effects of a misspecified reward function. Our approach allows the user to provide trajectory-level feedback on agent's behavior during training, which can be integrated as a reward shaping signal in the following training iteration. We also allow the user to provide explanations of their feedback, which are used to augment the feedback and reduce user effort and feedback frequency. We evaluate ITERS in three environments and show that it can successfully correct misspecified reward functions.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 11:45:40 GMT" } ]
1,693,440,000,000
[ [ "Gajcin", "Jasmina", "" ], [ "McCarthy", "James", "" ], [ "Nair", "Rahul", "" ], [ "Marinescu", "Radu", "" ], [ "Daly", "Elizabeth", "" ], [ "Dusparic", "Ivana", "" ] ]
2308.15985
Jianwu Fang
Jianwu Fang, iahuan Qiao, Jianru Xue, and Zhengguo Li
Vision-Based Traffic Accident Detection and Anticipation: A Survey
accepted in IEEE Transactions on Circuits and Systems for Video Technology; 16 pages, 155 references
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Traffic accident detection and anticipation is an obstinate road safety problem and painstaking efforts have been devoted. With the rapid growth of video data, Vision-based Traffic Accident Detection and Anticipation (named Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving and surveillance safety. However, the long-tailed, unbalanced, highly dynamic, complex, and uncertain properties of traffic accidents form the Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI development may focus on these OOD but important problems. What has been done for Vision-TAD and Vision-TAA? What direction we should focus on in the future for this problem? A comprehensive survey is important. We present the first survey on Vision-TAD in the deep learning era and the first-ever survey for Vision-TAA. The pros and cons of each research prototype are discussed in detail during the investigation. In addition, we also provide a critical review of 31 publicly available benchmarks and related evaluation metrics. Through this survey, we want to spawn new insights and open possible trends for Vision-TAD and Vision-TAA tasks.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 12:13:41 GMT" } ]
1,693,440,000,000
[ [ "Fang", "Jianwu", "" ], [ "Qiao", "iahuan", "" ], [ "Xue", "Jianru", "" ], [ "Li", "Zhengguo", "" ] ]
2308.16262
Kiet Vo
Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau, Krikamol Muandet
Causal Strategic Learning with Competitive Selection
Added more discussions on assumptions and the algorithm, and expand the Conclusion
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it. Firstly, while much of prior work focuses on studying a fixed pool of agents that remains static regardless of their evaluations, we consider the impact of selection procedure by which agents are not only evaluated, but also selected. When each decision maker unilaterally selects agents by maximising their own utility, we show that the optimal selection rule is a trade-off between selecting the best agents and providing incentives to maximise the agents' improvement. Furthermore, this optimal selection rule relies on incorrect predictions of agents' outcomes. Hence, we study the conditions under which a decision maker's optimal selection rule will not lead to deterioration of agents' outcome nor cause unjust reduction in agents' selection chance. To that end, we provide an analytical form of the optimal selection rule and a mechanism to retrieve the causal parameters from observational data, under certain assumptions on agents' behaviour. Secondly, when there are multiple decision makers, the interference between selection rules introduces another source of biases in estimating the underlying causal parameters. To address this problem, we provide a cooperative protocol which all decision makers must collectively adopt to recover the true causal parameters. Lastly, we complement our theoretical results with simulation studies. Our results highlight not only the importance of causal modeling as a strategy to mitigate the effect of gaming, as suggested by previous work, but also the need of a benevolent regulator to enable it.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 18:43:11 GMT" }, { "version": "v2", "created": "Tue, 5 Sep 2023 09:17:11 GMT" }, { "version": "v3", "created": "Sat, 3 Feb 2024 22:44:45 GMT" } ]
1,707,177,600,000
[ [ "Vo", "Kiet Q. H.", "" ], [ "Aadil", "Muneeb", "" ], [ "Chau", "Siu Lun", "" ], [ "Muandet", "Krikamol", "" ] ]
2308.16328
Li He
Li He, Siyi Hu, Ailun Pei
Debunking Disinformation: Revolutionizing Truth with NLP in Fake News Detection
The content is not particularly relevant to the research
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
The Internet and social media have altered how individuals access news in the age of instantaneous information distribution. While this development has increased access to information, it has also created a significant problem: the spread of fake news and information. Fake news is rapidly spreading on digital platforms, which has a negative impact on the media ecosystem, public opinion, decision-making, and social cohesion. Natural Language Processing(NLP), which offers a variety of approaches to identify content as authentic, has emerged as a potent weapon in the growing war against disinformation. This paper takes an in-depth look at how NLP technology can be used to detect fake news and reveals the challenges and opportunities it presents.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 21:25:31 GMT" }, { "version": "v2", "created": "Wed, 15 Nov 2023 20:56:25 GMT" } ]
1,700,179,200,000
[ [ "He", "Li", "" ], [ "Hu", "Siyi", "" ], [ "Pei", "Ailun", "" ] ]
2308.16364
Matija Franklin
Matija Franklin, Philip Moreira Tomei, Rebecca Gorman
Strengthening the EU AI Act: Defining Key Terms on AI Manipulation
10 pages
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The European Union's Artificial Intelligence Act aims to regulate manipulative and harmful uses of AI, but lacks precise definitions for key concepts. This paper provides technical recommendations to improve the Act's conceptual clarity and enforceability. We review psychological models to define "personality traits," arguing the Act should protect full "psychometric profiles." We urge expanding "behavior" to include "preferences" since preferences causally influence and are influenced by behavior. Clear definitions are provided for "subliminal," "manipulative," and "deceptive" techniques, considering incentives, intent, and covertness. We distinguish "exploiting individuals" from "exploiting groups," emphasising different policy needs. An "informed decision" is defined by four facets: comprehension, accurate information, no manipulation, and understanding AI's influence. We caution the Act's therapeutic use exemption given the lack of regulation of digital therapeutics by the EMA. Overall, the recommendations strengthen definitions of vague concepts in the EU AI Act, enhancing precise applicability to regulate harmful AI manipulation.
[ { "version": "v1", "created": "Wed, 30 Aug 2023 23:42:07 GMT" } ]
1,693,526,400,000
[ [ "Franklin", "Matija", "" ], [ "Tomei", "Philip Moreira", "" ], [ "Gorman", "Rebecca", "" ] ]
2308.16441
Chen Zhao
Dong Li, Wenjun Wang, Minglai Shao, Chen Zhao
Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
CIKM 2023
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 04:04:09 GMT" } ]
1,693,526,400,000
[ [ "Li", "Dong", "" ], [ "Wang", "Wenjun", "" ], [ "Shao", "Minglai", "" ], [ "Zhao", "Chen", "" ] ]
2308.16538
Carsten Maple
Carsten Maple, Lukasz Szpruch, Gregory Epiphaniou, Kalina Staykova, Simran Singh, William Penwarden, Yisi Wen, Zijian Wang, Jagdish Hariharan, Pavle Avramovic
The AI Revolution: Opportunities and Challenges for the Finance Sector
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 08:30:09 GMT" } ]
1,693,526,400,000
[ [ "Maple", "Carsten", "" ], [ "Szpruch", "Lukasz", "" ], [ "Epiphaniou", "Gregory", "" ], [ "Staykova", "Kalina", "" ], [ "Singh", "Simran", "" ], [ "Penwarden", "William", "" ], [ "Wen", "Yisi", "" ], [ "Wang", "Zijian", "" ], [ "Hariharan", "Jagdish", "" ], [ "Avramovic", "Pavle", "" ] ]
2308.16596
Victor Qu\'etu
Victor Qu\'etu and Marta Milovanovi\'c
The Quest of Finding the Antidote to Sparse Double Descent
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In energy-efficient schemes, finding the optimal size of deep learning models is very important and has a broad impact. Meanwhile, recent studies have reported an unexpected phenomenon, the sparse double descent: as the model's sparsity increases, the performance first worsens, then improves, and finally deteriorates. Such a non-monotonic behavior raises serious questions about the optimal model's size to maintain high performance: the model needs to be sufficiently over-parametrized, but having too many parameters wastes training resources. In this paper, we aim to find the best trade-off efficiently. More precisely, we tackle the occurrence of the sparse double descent and present some solutions to avoid it. Firstly, we show that a simple $\ell_2$ regularization method can help to mitigate this phenomenon but sacrifices the performance/sparsity compromise. To overcome this problem, we then introduce a learning scheme in which distilling knowledge regularizes the student model. Supported by experimental results achieved using typical image classification setups, we show that this approach leads to the avoidance of such a phenomenon.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 09:56:40 GMT" } ]
1,693,526,400,000
[ [ "Quétu", "Victor", "" ], [ "Milovanović", "Marta", "" ] ]
2308.16615
Lossan Bonde
Lossan Bonde, Severin Dembele
High Accuracy Location Information Extraction from Social Network Texts Using Natural Language Processing
null
International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
10.5121/ijnlc.2023.12401
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terrorism has become a worldwide plague with severe consequences for the development of nations. Besides killing innocent people daily and preventing educational activities from taking place, terrorism is also hindering economic growth. Machine Learning (ML) and Natural Language Processing (NLP) can contribute to fighting terrorism by predicting in real-time future terrorist attacks if accurate data is available. This paper is part of a research project that uses text from social networks to extract necessary information to build an adequate dataset for terrorist attack prediction. We collected a set of 3000 social network texts about terrorism in Burkina Faso and used a subset to experiment with existing NLP solutions. The experiment reveals that existing solutions have poor accuracy for location recognition, which our solution resolves. We will extend the solution to extract dates and action information to achieve the project's goal.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 10:21:24 GMT" } ]
1,693,526,400,000
[ [ "Bonde", "Lossan", "" ], [ "Dembele", "Severin", "" ] ]
2308.16879
Chen Zhao
Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen
Adaptation Speed Analysis for Fairness-aware Causal Models
CIKM 2023
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models, respectively, exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 17:36:57 GMT" } ]
1,693,526,400,000
[ [ "Lin", "Yujie", "" ], [ "Zhao", "Chen", "" ], [ "Shao", "Minglai", "" ], [ "Zhao", "Xujiang", "" ], [ "Chen", "Haifeng", "" ] ]
2309.00138
Pakizar Shamoi Dr
Pavel Kozlov, Alisher Akram, Pakizar Shamoi
Fuzzy Approach for Audio-Video Emotion Recognition in Computer Games for Children
8 pages. Prepared for the Elsevier conference
null
10.1016/j.procs.2023.12.139
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Computer games are widespread nowadays and enjoyed by people of all ages. But when it comes to kids, playing these games can be more than just fun, it is a way for them to develop important skills and build emotional intelligence. Facial expressions and sounds that kids produce during gameplay reflect their feelings, thoughts, and moods. In this paper, we propose a novel framework that integrates a fuzzy approach for the recognition of emotions through the analysis of audio and video data. Our focus lies within the specific context of computer games tailored for children, aiming to enhance their overall user experience. We use the FER dataset to detect facial emotions in video frames recorded from the screen during the game. For the audio emotion recognition of sounds a kid produces during the game, we use CREMA-D, TESS, RAVDESS, and Savee datasets. Next, a fuzzy inference system is used for the fusion of results. Besides this, our system can detect emotion stability and emotion diversity during gameplay, which, together with prevailing emotion report, can serve as valuable information for parents worrying about the effect of certain games on their kids. The proposed approach has shown promising results in the preliminary experiments we conducted, involving 3 different video games, namely fighting, racing, and logic games, and providing emotion-tracking results for kids in each game. Our study can contribute to the advancement of child-oriented game development, which is not only engaging but also accounts for children's cognitive and emotional states.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 21:22:00 GMT" } ]
1,705,881,600,000
[ [ "Kozlov", "Pavel", "" ], [ "Akram", "Alisher", "" ], [ "Shamoi", "Pakizar", "" ] ]
2309.00172
Thayanne Fran\c{c}a Da Silva
T. F. Silva and J. E. B. Maia
Detecting Evidence of Organization in groups by Trajectories
17 pages, 16 figures, 3 algorithms, 1 table
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Effective detection of organizations is essential for fighting crime and maintaining public safety, especially considering the limited human resources and tools to deal with each group that exhibits co-movement patterns. This paper focuses on solving the Network Structure Inference (NSI) challenge. Thus, we introduce two new approaches to detect network structure inferences based on agent trajectories. The first approach is based on the evaluation of graph entropy, while the second considers the quality of clustering indices. To evaluate the effectiveness of the new approaches, we conducted experiments using four scenario simulations based on the animal kingdom, available on the NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation. Furthermore, we compare the results obtained with those of an approach previously proposed in the literature, applying all methods to simulations of the NetLogo platform. The results demonstrate that our new detection approaches can more clearly identify the inferences of organizations or networks in the simulated scenarios.
[ { "version": "v1", "created": "Thu, 31 Aug 2023 23:57:02 GMT" } ]
1,693,785,600,000
[ [ "Silva", "T. F.", "" ], [ "Maia", "J. E. B.", "" ] ]
2309.00300
Jiatong Li
Jiatong Li, Qi Liu, Fei Wang, Jiayu Liu, Zhenya Huang, Fangzhou Yao, Linbo Zhu, Yu Su
Towards the Identifiability and Explainability for Personalized Learner Modeling: An Inductive Paradigm
Accepted by the ACM Web Conference 2024 (WWW '24)
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow the proficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novel response-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness.
[ { "version": "v1", "created": "Fri, 1 Sep 2023 07:18:02 GMT" }, { "version": "v2", "created": "Thu, 1 Feb 2024 14:34:31 GMT" }, { "version": "v3", "created": "Fri, 2 Feb 2024 03:53:12 GMT" }, { "version": "v4", "created": "Mon, 19 Feb 2024 15:01:33 GMT" } ]
1,708,387,200,000
[ [ "Li", "Jiatong", "" ], [ "Liu", "Qi", "" ], [ "Wang", "Fei", "" ], [ "Liu", "Jiayu", "" ], [ "Huang", "Zhenya", "" ], [ "Yao", "Fangzhou", "" ], [ "Zhu", "Linbo", "" ], [ "Su", "Yu", "" ] ]
2309.00306
Patrick Betz
Patrick Betz, Stefan L\"udtke, Christian Meilicke, Heiner Stuckenschmidt
On the Aggregation of Rules for Knowledge Graph Completion
KLR Workshop@ICML2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result in noisy and large rulesets, it is underrepresented in the literature and its theoretical foundations have not been studied before in this context. In this work, we demonstrate that existing aggregation approaches can be expressed as marginal inference operations over the predicting rules. In particular, we show that the common Max-aggregation strategy, which scores candidates based on the rule with the highest confidence, has a probabilistic interpretation. Finally, we propose an efficient and overlooked baseline which combines the previous strategies and is competitive to computationally more expensive approaches.
[ { "version": "v1", "created": "Fri, 1 Sep 2023 07:32:11 GMT" } ]
1,693,785,600,000
[ [ "Betz", "Patrick", "" ], [ "Lüdtke", "Stefan", "" ], [ "Meilicke", "Christian", "" ], [ "Stuckenschmidt", "Heiner", "" ] ]
2309.00317
Son T. Luu
Anh Hoang Tran, Tam Minh Nguyen and Son T. Luu
A Text-based Approach For Link Prediction on Wikipedia Articles
Accepted by DSAA 2023 Conference in the DSAA Student Competition Section
null
10.1109/DSAA60987.2023.10302627
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
This paper present our work in the DSAA 2023 Challenge about Link Prediction for Wikipedia Articles. We use traditional machine learning models with POS tags (part-of-speech tags) features extracted from text to train the classification model for predicting whether two nodes has the link. Then, we use these tags to test on various machine learning models. We obtained the results by F1 score at 0.99999 and got 7th place in the competition. Our source code is publicly available at this link: https://github.com/Tam1032/DSAA2023-Challenge-Link-prediction-DS-UIT_SAT
[ { "version": "v1", "created": "Fri, 1 Sep 2023 08:00:43 GMT" }, { "version": "v2", "created": "Tue, 7 Nov 2023 03:32:14 GMT" } ]
1,699,401,600,000
[ [ "Tran", "Anh Hoang", "" ], [ "Nguyen", "Tam Minh", "" ], [ "Luu", "Son T.", "" ] ]
2309.01194
Haomin Wen
Haomin Wen, Youfang Lin, Lixia Wu, Xiaowei Mao, Tianyue Cai, Yunfeng Hou, Shengnan Guo, Yuxuan Liang, Guangyin Jin, Yiji Zhao, Roger Zimmermann, Jieping Ye, Huaiyu Wan
A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route\&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route\&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field.
[ { "version": "v1", "created": "Sun, 3 Sep 2023 14:43:33 GMT" } ]
1,693,958,400,000
[ [ "Wen", "Haomin", "" ], [ "Lin", "Youfang", "" ], [ "Wu", "Lixia", "" ], [ "Mao", "Xiaowei", "" ], [ "Cai", "Tianyue", "" ], [ "Hou", "Yunfeng", "" ], [ "Guo", "Shengnan", "" ], [ "Liang", "Yuxuan", "" ], [ "Jin", "Guangyin", "" ], [ "Zhao", "Yiji", "" ], [ "Zimmermann", "Roger", "" ], [ "Ye", "Jieping", "" ], [ "Wan", "Huaiyu", "" ] ]
2309.01622
Mla{\dj}an Jovanovi\'c Dr
Peter Voss and Mladjan Jovanovic
Concepts is All You Need: A More Direct Path to AGI
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Little demonstrable progress has been made toward AGI (Artificial General Intelligence) since the term was coined some 20 years ago. In spite of the fantastic breakthroughs in Statistical AI such as AlphaZero, ChatGPT, and Stable Diffusion none of these projects have, or claim to have, a clear path to AGI. In order to expedite the development of AGI it is crucial to understand and identify the core requirements of human-like intelligence as it pertains to AGI. From that one can distill which particular development steps are necessary to achieve AGI, and which are a distraction. Such analysis highlights the need for a Cognitive AI approach rather than the currently favored statistical and generative efforts. More specifically it identifies the central role of concepts in human-like cognition. Here we outline an architecture and development plan, together with some preliminary results, that offers a much more direct path to full Human-Level AI (HLAI)/ AGI.
[ { "version": "v1", "created": "Mon, 4 Sep 2023 14:14:41 GMT" } ]
1,693,958,400,000
[ [ "Voss", "Peter", "" ], [ "Jovanovic", "Mladjan", "" ] ]
2309.02009
Florence Dupin de Saint-Cyr
Florence Dupin de Saint Cyr - Bannay (IRIT-ADRIA), Henri Prade (IRIT-ADRIA)
Belief revision and incongruity: is it a joke?
A special paper on/in humor/honor for/of Philippe Besnard
Journal of Applied Non-Classical Logics, In press, Special issue in honour of Philippe Besnard, pp.1-28
10.1080/11663081.2023.2244379
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Incongruity often makes people laugh. You have to be smart to say stupid things. It requires to be even smarter for understanding them. This paper is a shameless attempt to formalize this intelligent behavior in the case of an agent listening to a joke. All this is a matter of revision of beliefs, surprise and violation of norms.
[ { "version": "v1", "created": "Tue, 5 Sep 2023 07:47:08 GMT" } ]
1,693,958,400,000
[ [ "Bannay", "Florence Dupin de Saint Cyr -", "", "IRIT-ADRIA" ], [ "Prade", "Henri", "", "IRIT-ADRIA" ] ]
2309.02287
Kim Hammar
Kim Hammar and Neil Dhir
Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
We consider the problem of optimising an expensive-to-evaluate grey-box objective function, within a finite budget, where known side-information exists in the form of the causal structure between the design variables. Standard black-box optimisation ignores the causal structure, often making it inefficient and expensive. The few existing methods that consider the causal structure are myopic and do not fully accommodate the observation-intervention trade-off that emerges when estimating causal effects. In this paper, we show that the observation-intervention trade-off can be formulated as a non-myopic optimal stopping problem which permits an efficient solution. We give theoretical results detailing the structure of the optimal stopping times and demonstrate the generality of our approach by showing that it can be integrated with existing causal Bayesian optimisation algorithms. Experimental results show that our formulation can enhance existing algorithms on real and synthetic benchmarks.
[ { "version": "v1", "created": "Tue, 5 Sep 2023 14:46:06 GMT" } ]
1,693,958,400,000
[ [ "Hammar", "Kim", "" ], [ "Dhir", "Neil", "" ] ]
2309.03041
Xuanxiang Huang
Xuanxiang Huang, Joao Marques-Silva
A Refutation of Shapley Values for Explainability
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Recent work demonstrated the existence of Boolean functions for which Shapley values provide misleading information about the relative importance of features in rule-based explanations. Such misleading information was broadly categorized into a number of possible issues. Each of those issues relates with features being relevant or irrelevant for a prediction, and all are significant regarding the inadequacy of Shapley values for rule-based explainability. This earlier work devised a brute-force approach to identify Boolean functions, defined on small numbers of features, and also associated instances, which displayed such inadequacy-revealing issues, and so served as evidence to the inadequacy of Shapley values for rule-based explainability. However, an outstanding question is how frequently such inadequacy-revealing issues can occur for Boolean functions with arbitrary large numbers of features. It is plain that a brute-force approach would be unlikely to provide insights on how to tackle this question. This paper answers the above question by proving that, for any number of features, there exist Boolean functions that exhibit one or more inadequacy-revealing issues, thereby contributing decisive arguments against the use of Shapley values as the theoretical underpinning of feature-attribution methods in explainability.
[ { "version": "v1", "created": "Wed, 6 Sep 2023 14:34:18 GMT" }, { "version": "v2", "created": "Tue, 13 Feb 2024 07:35:25 GMT" } ]
1,707,868,800,000
[ [ "Huang", "Xuanxiang", "" ], [ "Marques-Silva", "Joao", "" ] ]
2309.03638
Yulu Pi
Yulu Pi
Beyond XAI:Obstacles Towards Responsible AI
work in progress
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The rapidly advancing domain of Explainable Artificial Intelligence (XAI) has sparked significant interests in developing techniques to make AI systems more transparent and understandable. Nevertheless, in real-world contexts, the methods of explainability and their evaluation strategies present numerous limitations.Moreover, the scope of responsible AI extends beyond just explainability. In this paper, we explore these limitations and discuss their implications in a boarder context of responsible AI when considering other important aspects, including privacy, fairness and contestability.
[ { "version": "v1", "created": "Thu, 7 Sep 2023 11:08:14 GMT" } ]
1,694,131,200,000
[ [ "Pi", "Yulu", "" ] ]
2309.03651
Manuel Eberhardinger
Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi
Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments
to be published in AIIDE 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effect is even more significant, as unexpected behavior cause accidents with potentially grave and long-lasting consequences for the involved individuals. In this work, we propose using program synthesis to imitate reinforcement learning policies after seeing a trajectory of the action sequence. Programs have the advantage that they are inherently interpretable and verifiable for correctness. We adapt the state-of-the-art program synthesis system DreamCoder for learning concepts in grid-based environments, specifically, a navigation task and two miniature versions of Atari games, Space Invaders and Asterix. By inspecting the generated libraries, we can make inferences about the concepts the black-box agent has learned and better understand the agent's behavior. We achieve the same by visualizing the agent's decision-making process for the imitated sequences. We evaluate our approach with different types of program synthesizers based on a search-only method, a neural-guided search, and a language model fine-tuned on code.
[ { "version": "v1", "created": "Thu, 7 Sep 2023 11:46:57 GMT" } ]
1,694,131,200,000
[ [ "Eberhardinger", "Manuel", "" ], [ "Maucher", "Johannes", "" ], [ "Maghsudi", "Setareh", "" ] ]
2309.04295
Chengwu Liu
Chengwu Liu, Jianhao Shen, Huajian Xin, Zhengying Liu, Ye Yuan, Haiming Wang, Wei Ju, Chuanyang Zheng, Yichun Yin, Lin Li, Ming Zhang, Qun Liu
FIMO: A Challenge Formal Dataset for Automated Theorem Proving
Added a hyperlink to the dataset made accessible on GitHub
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems. Designed to facilitate advanced automated theorem proving at the IMO level, FIMO is currently tailored for the Lean formal language. It comprises 149 formal problem statements, accompanied by both informal problem descriptions and their corresponding LaTeX-based informal proofs. Through initial experiments involving GPT-4, our findings underscore the existing limitations in current methodologies, indicating a substantial journey ahead before achieving satisfactory IMO-level automated theorem proving outcomes.
[ { "version": "v1", "created": "Fri, 8 Sep 2023 12:34:28 GMT" }, { "version": "v2", "created": "Tue, 5 Dec 2023 08:38:01 GMT" } ]
1,701,820,800,000
[ [ "Liu", "Chengwu", "" ], [ "Shen", "Jianhao", "" ], [ "Xin", "Huajian", "" ], [ "Liu", "Zhengying", "" ], [ "Yuan", "Ye", "" ], [ "Wang", "Haiming", "" ], [ "Ju", "Wei", "" ], [ "Zheng", "Chuanyang", "" ], [ "Yin", "Yichun", "" ], [ "Li", "Lin", "" ], [ "Zhang", "Ming", "" ], [ "Liu", "Qun", "" ] ]
2309.05371
Jean-Baptiste Herv\'e
Jean-Baptiste Herv\'e, Oliver Withington, Marion Herv\'e, Laurissa Tokarchuk, Christoph Salge
Exploring Minecraft Settlement Generators with Generative Shift Analysis
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines.
[ { "version": "v1", "created": "Mon, 11 Sep 2023 10:48:42 GMT" } ]
1,694,476,800,000
[ [ "Hervé", "Jean-Baptiste", "" ], [ "Withington", "Oliver", "" ], [ "Hervé", "Marion", "" ], [ "Tokarchuk", "Laurissa", "" ], [ "Salge", "Christoph", "" ] ]
2309.06888
Konrad Abicht
Konrad Abicht
OWL Reasoners still useable in 2023
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
In a systematic literature and software review over 100 OWL reasoners/systems were analyzed to see if they would still be usable in 2023. This has never been done in this capacity. OWL reasoners still play an important role in knowledge organisation and management, but the last comprehensive surveys/studies are more than 8 years old. The result of this work is a comprehensive list of 95 standalone OWL reasoners and systems using an OWL reasoner. For each item, information on project pages, source code repositories and related documentation was gathered. The raw research data is provided in a Github repository for anyone to use.
[ { "version": "v1", "created": "Wed, 13 Sep 2023 11:22:42 GMT" } ]
1,694,649,600,000
[ [ "Abicht", "Konrad", "" ] ]
2309.08611
Hongpeng Zhang
Zhang Hong-Peng
Maneuver Decision-Making Through Proximal Policy Optimization And Monte Carlo Tree Search
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Maneuver decision-making can be regarded as a Markov decision process and can be address by reinforcement learning. However, original reinforcement learning algorithms can hardly solve the maneuvering decision-making problem. One reason is that agents use random actions in the early stages of training, which makes it difficult to get rewards and learn how to make effective decisions. To address this issue, a method based on proximal policy optimization and Monte Carlo tree search is proposed. The method uses proximal policy optimization to train the agent, and regards the results of air combat as targets to train the value network. Then, based on the value network and the visit count of each node, Monte Carlo tree search is used to find the actions with more expected returns than random actions, which can improve the training performance. The ablation studies and simulation experiments indicate that agents trained by the proposed method can make different decisions according to different states, which demonstrates that the method can solve the maneuvering decision problem that the original reinforcement learning algorithm cannot solve.
[ { "version": "v1", "created": "Mon, 28 Aug 2023 14:48:49 GMT" } ]
1,695,081,600,000
[ [ "Hong-Peng", "Zhang", "" ] ]
2309.08978
Shiqi Jiang
Fucheng Jia, Shiqi Jiang, Ting Cao, Wei Cui, Tianrui Xia, Xu Cao, Yuanchun Li, Deyu Zhang, Ju Ren, Yunxin Liu, Lili Qiu, Mao Yang
Accelerating In-Browser Deep Learning Inference on Diverse Edge Clients through Just-in-Time Kernel Optimizations
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Web applications are increasingly becoming the primary platform for AI service delivery, making in-browser deep learning (DL) inference more prominent. However, current in-browser inference systems fail to effectively utilize advanced web programming techniques and customize kernels for various client devices, leading to suboptimal performance. To address the issues, this paper presents the first in-browser inference system, nn-JIT.web, which enables just-in-time (JIT) auto-generation of optimized kernels for both CPUs and GPUs during inference. The system achieves this by using two novel web programming techniques that can significantly reduce kernel generation time, compared to other tensor compilers such as TVM, while maintaining or even improving performance. The first technique, Tensor-Web Compiling Co-Design, lowers compiling costs by unifying tensor and web compiling and eliminating redundant and ineffective compiling passes. The second technique, Web-Specific Lite Kernel Optimization Space Design, reduces kernel tuning costs by focusing on web programming requirements and efficient hardware resource utilization, limiting the optimization space to only dozens. nn-JIT.web is evaluated for modern transformer models on a range of client devices, including the mainstream CPUs and GPUs from ARM, Intel, AMD and Nvidia. Results show that nn-JIT.web can achieve up to 8.2x faster within 30 seconds compared to the baselines across various models.
[ { "version": "v1", "created": "Sat, 16 Sep 2023 12:29:25 GMT" } ]
1,695,081,600,000
[ [ "Jia", "Fucheng", "" ], [ "Jiang", "Shiqi", "" ], [ "Cao", "Ting", "" ], [ "Cui", "Wei", "" ], [ "Xia", "Tianrui", "" ], [ "Cao", "Xu", "" ], [ "Li", "Yuanchun", "" ], [ "Zhang", "Deyu", "" ], [ "Ren", "Ju", "" ], [ "Liu", "Yunxin", "" ], [ "Qiu", "Lili", "" ], [ "Yang", "Mao", "" ] ]
2309.09125
Anas El Fathi
Anas El Fathi, Marc D. Breton
Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments
6 pages, 4 figures, conference
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime, especially when under multiple daily injections (MDI) therapy. Effectively, they will not always perform rigorous and precise calculations, but occasionally, they might rely on intuition and previous experience. Reinforcement learning (RL) has shown outstanding results in outperforming humans on tasks requiring intuition and learning from experience. In this work, we propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy that does not require precise carbohydrate counting (CC) (e.g., a usual meal at noon.). The agent is trained using the soft actor-critic approach and comprises long short-term memory (LSTM) neurons. For training, eighty virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were simulated using MDI therapy and QM strategy. For validation, the remaining twenty VS were examined in 26-week scenarios, including intra- and inter-day variabilities in glucose. \textit{In-silico} results showed that the proposed RL approach outperforms a baseline run-to-run approach and can replace the standard CC approach. Specifically, after 26 weeks, the time-in-range ($70-180$mg/dL) and time-in-hypoglycemia ($<70$mg/dL) were $73.1\pm11.6$% and $ 2.0\pm 1.8$% using the RL-optimized QM strategy compared to $70.6\pm14.8$% and $ 1.5\pm 1.5$% using CC. Such an approach can simplify diabetes treatment, resulting in improved quality of life and glycemic outcomes.
[ { "version": "v1", "created": "Sun, 17 Sep 2023 01:34:02 GMT" } ]
1,695,081,600,000
[ [ "Fathi", "Anas El", "" ], [ "Breton", "Marc D.", "" ] ]
2309.09404
Siva Likitha Valluru
Siva Likitha Valluru, Biplav Srivastava, Sai Teja Paladi, Siwen Yan, Sriraam Natarajan
Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals
9 pages, 2 figures, 3 tables, Accepted to The Thirty-Sixth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI/AAAI-24)
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Building teams and promoting collaboration are two very common business activities. An example of these are seen in the TeamingForFunding problem, where research institutions and researchers are interested to identify collaborative opportunities when applying to funding agencies in response to latter's calls for proposals. We describe a novel system to recommend teams using a variety of AI methods, such that (1) each team achieves the highest possible skill coverage that is demanded by the opportunity, and (2) the workload of distributing the opportunities is balanced amongst the candidate members. We address these questions by extracting skills latent in open data of proposal calls (demand) and researcher profiles (supply), normalizing them using taxonomies, and creating efficient algorithms that match demand to supply. We create teams to maximize goodness along a novel metric balancing short- and long-term objectives. We validate the success of our algorithms (1) quantitatively, by evaluating the recommended teams using a goodness score and find that more informed methods lead to recommendations of smaller number of teams but higher goodness, and (2) qualitatively, by conducting a large-scale user study at a college-wide level, and demonstrate that users overall found the tool very useful and relevant. Lastly, we evaluate our system in two diverse settings in US and India (of researchers and proposal calls) to establish generality of our approach, and deploy it at a major US university for routine use.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 00:04:08 GMT" }, { "version": "v2", "created": "Wed, 27 Sep 2023 05:55:14 GMT" }, { "version": "v3", "created": "Tue, 24 Oct 2023 03:21:32 GMT" }, { "version": "v4", "created": "Fri, 5 Jan 2024 04:54:24 GMT" }, { "version": "v5", "created": "Thu, 25 Jan 2024 16:22:56 GMT" } ]
1,706,227,200,000
[ [ "Valluru", "Siva Likitha", "" ], [ "Srivastava", "Biplav", "" ], [ "Paladi", "Sai Teja", "" ], [ "Yan", "Siwen", "" ], [ "Natarajan", "Sriraam", "" ] ]
2309.09416
Gilles Blondel
Gilles Blondel
Causal Discovery and Prediction: Methods and Algorithms
PhD Thesis, 101 pages. arXiv admin note: text overlap with arXiv:1610.05556
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We are not only observers but also actors of reality. Our capability to intervene and alter the course of some events in the space and time surrounding us is an essential component of how we build our model of the world. In this doctoral thesis we introduce a generic a-priori assessment of each possible intervention, in order to select the most cost-effective interventions only, and avoid unnecessary systematic experimentation on the real world. Based on this a-priori assessment, we propose an active learning algorithm that identifies the causal relations in any given causal model, using a least cost sequence of interventions. There are several novel aspects introduced by our algorithm. It is, in most case scenarios, able to discard many causal model candidates using relatively inexpensive interventions that only test one value of the intervened variables. Also, the number of interventions performed by the algorithm can be bounded by the number of causal model candidates. Hence, fewer initial candidates (or equivalently, more prior knowledge) lead to fewer interventions for causal discovery. Causality is intimately related to time, as causes appear to precede their effects. Cyclical causal processes are a very interesting case of causality in relation to time. In this doctoral thesis we introduce a formal analysis of time cyclical causal settings by defining a causal analog to the purely observational Dynamic Bayesian Networks, and provide a sound and complete algorithm for the identification of causal effects in the cyclic setting. We introduce the existence of two types of hidden confounder variables in this framework, which affect in substantially different ways the identification procedures, a distinction with no analog in either Dynamic Bayesian Networks or standard causal graphs.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 01:19:37 GMT" } ]
1,695,081,600,000
[ [ "Blondel", "Gilles", "" ] ]
2309.09441
Hossein Jamali
Hossein Jamali, Ponkoj Chandra Shill, David Feil-Seifer, Frederick C. Harris, Jr., Sergiu M. Dascalu
A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm
15 pages, 6 figures, 2023 IFIP International Internet of Things Conference. Dallas-Fort Worth Metroplex, Texas, USA
null
10.1007/978-3-031-45878-1_5
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper, one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 02:48:41 GMT" } ]
1,708,473,600,000
[ [ "Jamali", "Hossein", "" ], [ "Shill", "Ponkoj Chandra", "" ], [ "Feil-Seifer", "David", "" ], [ "Harris,", "Frederick C.", "Jr." ], [ "Dascalu", "Sergiu M.", "" ] ]
2309.09500
Zijian Zhang
Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang and Zitao Liu
PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In the era of information explosion, spatio-temporal data mining serves as a critical part of urban management. Considering the various fields demanding attention, e.g., traffic state, human activity, and social event, predicting multiple spatio-temporal attributes simultaneously can alleviate regulatory pressure and foster smart city construction. However, current research can not handle the spatio-temporal multi-attribute prediction well due to the complex relationships between diverse attributes. The key challenge lies in how to address the common spatio-temporal patterns while tackling their distinctions. In this paper, we propose an effective solution for spatio-temporal multi-attribute prediction, PromptST. We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes. Then, we elaborate a spatio-temporal prompt tuning strategy to fit the specific attributes in a lightweight manner. Through the pretrain and prompt tuning phases, our PromptST is able to enhance the specific spatio-temoral characteristic capture by prompting the backbone model to fit the specific target attribute while maintaining the learned common knowledge. Extensive experiments on real-world datasets verify that our PromptST attains state-of-the-art performance. Furthermore, we also prove PromptST owns good transferability on unseen spatio-temporal attributes, which brings promising application potential in urban computing. The implementation code is available to ease reproducibility.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 05:57:12 GMT" } ]
1,695,081,600,000
[ [ "Zhang", "Zijian", "" ], [ "Zhao", "Xiangyu", "" ], [ "Liu", "Qidong", "" ], [ "Zhang", "Chunxu", "" ], [ "Ma", "Qian", "" ], [ "Wang", "Wanyu", "" ], [ "Zhao", "Hongwei", "" ], [ "Wang", "Yiqi", "" ], [ "Liu", "Zitao", "" ] ]
2309.09770
Carlos Mougan
Carlos Mougan, Richard Plant, Clare Teng, Marya Bazzi, Alvaro Cabrejas-Egea, Ryan Sze-Yin Chan, David Salvador Jasin, Martin Stoffel, Kirstie Jane Whitaker, Jules Manser
How to Data in Datathons
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from organizing >80 datathon challenges with >60 partnership organizations since 2016, we provide guidelines and recommendations that serve as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 13:51:23 GMT" }, { "version": "v2", "created": "Tue, 19 Sep 2023 15:44:27 GMT" }, { "version": "v3", "created": "Mon, 9 Oct 2023 12:43:06 GMT" }, { "version": "v4", "created": "Wed, 25 Oct 2023 10:20:20 GMT" } ]
1,698,278,400,000
[ [ "Mougan", "Carlos", "" ], [ "Plant", "Richard", "" ], [ "Teng", "Clare", "" ], [ "Bazzi", "Marya", "" ], [ "Cabrejas-Egea", "Alvaro", "" ], [ "Chan", "Ryan Sze-Yin", "" ], [ "Jasin", "David Salvador", "" ], [ "Stoffel", "Martin", "" ], [ "Whitaker", "Kirstie Jane", "" ], [ "Manser", "Jules", "" ] ]
2309.09825
Minjia Mao
Xiao Fang, Shangkun Che, Minjia Mao, Hongzhe Zhang, Ming Zhao, Xiaohang Zhao
Bias of AI-Generated Content: An Examination of News Produced by Large Language Models
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 14:47:24 GMT" }, { "version": "v2", "created": "Tue, 19 Sep 2023 01:13:22 GMT" }, { "version": "v3", "created": "Wed, 3 Apr 2024 19:47:54 GMT" } ]
1,712,275,200,000
[ [ "Fang", "Xiao", "" ], [ "Che", "Shangkun", "" ], [ "Mao", "Minjia", "" ], [ "Zhang", "Hongzhe", "" ], [ "Zhao", "Ming", "" ], [ "Zhao", "Xiaohang", "" ] ]
2309.09864
Daria de Tinguy
Daria de Tinguy, Toon Van de Maele, Tim Verbelen, Bart Dhoedt
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments
IROS 2023 Workshop World Models and Predictive Coding in Cognitive Robotics. arXiv admin note: text overlap with arXiv:2306.13546
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Cognitive maps play a crucial role in facilitating flexible behaviour by representing spatial and conceptual relationships within an environment. The ability to learn and infer the underlying structure of the environment is crucial for effective exploration and navigation. This paper introduces a hierarchical active inference model addressing the challenge of inferring structure in the world from pixel-based observations. We propose a three-layer hierarchical model consisting of a cognitive map, an allocentric, and an egocentric world model, combining curiosity-driven exploration with goal-oriented behaviour at the different levels of reasoning from context to place to motion. This allows for efficient exploration and goal-directed search in room-structured mini-grid environments.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 15:24:55 GMT" } ]
1,695,168,000,000
[ [ "de Tinguy", "Daria", "" ], [ "Van de Maele", "Toon", "" ], [ "Verbelen", "Tim", "" ], [ "Dhoedt", "Bart", "" ] ]
2309.09898
Simon Hosemann
Maurice Funk, Simon Hosemann, Jean Christoph Jung, Carsten Lutz
Towards Ontology Construction with Language Models
KBC-LM'23: Knowledge Base Construction from Pre-trained Language Models workshop at ISWC 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We present a method for automatically constructing a concept hierarchy for a given domain by querying a large language model. We apply this method to various domains using OpenAI's GPT 3.5. Our experiments indicate that LLMs can be of considerable help for constructing concept hierarchies.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 16:02:39 GMT" } ]
1,695,081,600,000
[ [ "Funk", "Maurice", "" ], [ "Hosemann", "Simon", "" ], [ "Jung", "Jean Christoph", "" ], [ "Lutz", "Carsten", "" ] ]
2309.09901
Sara Colantonio Ph.D.
Gianluca Carloni, Andrea Berti, Sara Colantonio
The role of causality in explainable artificial intelligence
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in computer science, even though the underlying concepts of causation and explanation share common ancient roots. This is further enforced by the lack of review works jointly covering these two fields. In this paper, we investigate the literature to try to understand how and to what extent causality and XAI are intertwined. More precisely, we seek to uncover what kinds of relationships exist between the two concepts and how one can benefit from them, for instance, in building trust in AI systems. As a result, three main perspectives are identified. In the first one, the lack of causality is seen as one of the major limitations of current AI and XAI approaches, and the "optimal" form of explanations is investigated. The second is a pragmatic perspective and considers XAI as a tool to foster scientific exploration for causal inquiry, via the identification of pursue-worthy experimental manipulations. Finally, the third perspective supports the idea that causality is propaedeutic to XAI in three possible manners: exploiting concepts borrowed from causality to support or improve XAI, utilizing counterfactuals for explainability, and considering accessing a causal model as explaining itself. To complement our analysis, we also provide relevant software solutions used to automate causal tasks. We believe our work provides a unified view of the two fields of causality and XAI by highlighting potential domain bridges and uncovering possible limitations.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 16:05:07 GMT" } ]
1,695,081,600,000
[ [ "Carloni", "Gianluca", "" ], [ "Berti", "Andrea", "" ], [ "Colantonio", "Sara", "" ] ]
2309.10129
Haochen Zhang
Haochen Zhang and Xi Chen and Lin F. Yang
Adaptive Liquidity Provision in Uniswap V3 with Deep Reinforcement Learning
15 pages, 5 figures, 9 tables, submitted to Financial Cryptography and Data Security 2024
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Decentralized exchanges (DEXs) are a cornerstone of decentralized finance (DeFi), allowing users to trade cryptocurrencies without the need for third-party authorization. Investors are incentivized to deposit assets into liquidity pools, against which users can trade directly, while paying fees to liquidity providers (LPs). However, a number of unresolved issues related to capital efficiency and market risk hinder DeFi's further development. Uniswap V3, a leading and groundbreaking DEX project, addresses capital efficiency by enabling LPs to concentrate their liquidity within specific price ranges for deposited assets. Nevertheless, this approach exacerbates market risk, as LPs earn trading fees only when asset prices are within these predetermined brackets. To mitigate this issue, this paper introduces a deep reinforcement learning (DRL) solution designed to adaptively adjust these price ranges, maximizing profits and mitigating market risks. Our approach also neutralizes price-change risks by hedging the liquidity position through a rebalancing portfolio in a centralized futures exchange. The DRL policy aims to optimize trading fees earned by LPs against associated costs, such as gas fees and hedging expenses, which is referred to as loss-versus-rebalancing (LVR). Using simulations with a profit-and-loss (PnL) benchmark, our method demonstrates superior performance in ETH/USDC and ETH/USDT pools compared to existing baselines. We believe that this strategy not only offers investors a valuable asset management tool but also introduces a new incentive mechanism for DEX designers.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 20:10:28 GMT" } ]
1,695,168,000,000
[ [ "Zhang", "Haochen", "" ], [ "Chen", "Xi", "" ], [ "Yang", "Lin F.", "" ] ]
2309.10209
Chen Zhao
Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen
Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Two prevalent types of distributional shifts in machine learning are the covariate shift (as observed across different domains) and the semantic shift (as seen across different classes). Traditional OOD detection techniques typically address only one of these shifts. However, real-world testing environments often present a combination of both covariate and semantic shifts. In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts. To this end, we introduce two regularization strategies: domain generalization regularization, which ensures semantic invariance across domains to counteract the covariate shift, and OOD detection regularization, designed to enhance OOD detection capabilities against the semantic shift through energy bounding. Through rigorous testing on three standard domain generalization benchmarks, our proposed framework showcases its superiority over conventional domain generalization approaches in terms of OOD detection performance. Moreover, it holds its ground by maintaining comparable InD classification accuracy.
[ { "version": "v1", "created": "Mon, 18 Sep 2023 23:48:22 GMT" } ]
1,695,168,000,000
[ [ "Wang", "Haoliang", "" ], [ "Zhao", "Chen", "" ], [ "Guo", "Yunhui", "" ], [ "Jiang", "Kai", "" ], [ "Chen", "Feng", "" ] ]
2309.10216
Shili Sheng
Shili Sheng, David Parker and Lu Feng
Safe POMDP Online Planning via Shielding
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Partially observable Markov decision processes (POMDPs) have been widely used in many robotic applications for sequential decision-making under uncertainty. POMDP online planning algorithms such as Partially Observable Monte-Carlo Planning (POMCP) can solve very large POMDPs with the goal of maximizing the expected return. But the resulting policies cannot provide safety guarantees which are imperative for real-world safety-critical tasks (e.g., autonomous driving). In this work, we consider safety requirements represented as almost-sure reach-avoid specifications (i.e., the probability to reach a set of goal states is one and the probability to reach a set of unsafe states is zero). We compute shields that restrict unsafe actions which would violate the almost-sure reach-avoid specifications. We then integrate these shields into the POMCP algorithm for safe POMDP online planning. We propose four distinct shielding methods, differing in how the shields are computed and integrated, including factored variants designed to improve scalability. Experimental results on a set of benchmark domains demonstrate that the proposed shielding methods successfully guarantee safety (unlike the baseline POMCP without shielding) on large POMDPs, with negligible impact on the runtime for online planning.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 00:02:05 GMT" }, { "version": "v2", "created": "Sat, 2 Mar 2024 15:49:21 GMT" } ]
1,709,596,800,000
[ [ "Sheng", "Shili", "" ], [ "Parker", "David", "" ], [ "Feng", "Lu", "" ] ]
2309.10253
Jiahao Yu
Jiahao Yu, Xingwei Lin, Zheng Yu, Xinyu Xing
GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Large language models (LLMs) have recently experienced tremendous popularity and are widely used from casual conversations to AI-driven programming. However, despite their considerable success, LLMs are not entirely reliable and can give detailed guidance on how to conduct harmful or illegal activities. While safety measures can reduce the risk of such outputs, adversarial jailbreak attacks can still exploit LLMs to produce harmful content. These jailbreak templates are typically manually crafted, making large-scale testing challenging. In this paper, we introduce GPTFuzz, a novel black-box jailbreak fuzzing framework inspired by the AFL fuzzing framework. Instead of manual engineering, GPTFuzz automates the generation of jailbreak templates for red-teaming LLMs. At its core, GPTFuzz starts with human-written templates as initial seeds, then mutates them to produce new templates. We detail three key components of GPTFuzz: a seed selection strategy for balancing efficiency and variability, mutate operators for creating semantically equivalent or similar sentences, and a judgment model to assess the success of a jailbreak attack. We evaluate GPTFuzz against various commercial and open-source LLMs, including ChatGPT, LLaMa-2, and Vicuna, under diverse attack scenarios. Our results indicate that GPTFuzz consistently produces jailbreak templates with a high success rate, surpassing human-crafted templates. Remarkably, GPTFuzz achieves over 90% attack success rates against ChatGPT and Llama-2 models, even with suboptimal initial seed templates. We anticipate that GPTFuzz will be instrumental for researchers and practitioners in examining LLM robustness and will encourage further exploration into enhancing LLM safety.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 02:19:48 GMT" }, { "version": "v2", "created": "Wed, 4 Oct 2023 06:15:12 GMT" } ]
1,696,464,000,000
[ [ "Yu", "Jiahao", "" ], [ "Lin", "Xingwei", "" ], [ "Yu", "Zheng", "" ], [ "Xing", "Xinyu", "" ] ]
2309.10293
Thanveer Shaik Mr
Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Juan D. Velasquez, Niall Higgins
QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems
This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-linear models like deep learning models has limited explainability due to its black-box nature. This can require decision-makers to make blind leaps of faith based on non-linear model results, especially in healthcare applications. In non-invasive monitoring, patient data from tracking sensors and their predisposing clinical attributes act as input features for predicting future vital signs. Explaining the contributions of various features to the overall output of the monitoring application is critical for a clinician's decision-making. In this study, an Explainable AI for Quantitative analysis (QXAI) framework is proposed with post-hoc model explainability and intrinsic explainability for regression and classification tasks in a supervised learning approach. This was achieved by utilizing the Shapley values concept and incorporating attention mechanisms in deep learning models. We adopted the artificial neural networks (ANN) and attention-based Bidirectional LSTM (BiLSTM) models for the prediction of heart rate and classification of physical activities based on sensor data. The deep learning models achieved state-of-the-art results in both prediction and classification tasks. Global explanation and local explanation were conducted on input data to understand the feature contribution of various patient data. The proposed QXAI framework was evaluated using PPG-DaLiA data to predict heart rate and mobile health (MHEALTH) data to classify physical activities based on sensor data. Monte Carlo approximation was applied to the framework to overcome the time complexity and high computation power requirements required for Shapley value calculations.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 03:50:30 GMT" }, { "version": "v2", "created": "Wed, 20 Sep 2023 03:02:24 GMT" }, { "version": "v3", "created": "Fri, 2 Feb 2024 08:07:40 GMT" } ]
1,707,091,200,000
[ [ "Shaik", "Thanveer", "" ], [ "Tao", "Xiaohui", "" ], [ "Xie", "Haoran", "" ], [ "Li", "Lin", "" ], [ "Velasquez", "Juan D.", "" ], [ "Higgins", "Niall", "" ] ]
2309.10324
Pooja Singhal
Bliss Singhal, Fnu Pooja
Metastatic Breast Cancer Prognostication Through Multimodal Integration of Dimensionality Reduction Algorithms and Classification Algorithms
10 pages, 14 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the point where the cancer has spread to other parts of the body and is the cause of approximately 90% of cancer related deaths. Normally, pathologists spend hours each day to manually classify whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of time and emphasizes the importance to be aware of human error, and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer saving thousands of lives and can also improve the speed and efficiency of the process thereby taking less resources and time. So far, deep learning methodology of AI has been used in the research to detect cancer. This study is a novel approach to determine the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm to reduce the dimensionality of the dataset, and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbors algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 05:12:02 GMT" } ]
1,695,168,000,000
[ [ "Singhal", "Bliss", "" ], [ "Pooja", "Fnu", "" ] ]
2309.10371
Benjamin Goertzel
Ben Goertzel
Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A moderately detailed consideration of interactive LLMs as cognitive systems is given, focusing on LLMs circa mid-2023 such as ChatGPT, GPT-4, Bard, Llama, etc.. Cognitive strengths of these systems are reviewed, and then careful attention is paid to the substantial differences between the sort of cognitive system these LLMs are, and the sort of cognitive systems human beings are. It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 07:12:55 GMT" } ]
1,695,168,000,000
[ [ "Goertzel", "Ben", "" ] ]
2309.10424
Vicent Blanes-Selva
Juan M. Garc\'ia-G\'omez, Vicent Blanes-Selva, Jos\'e Carlos de Bartolom\'e Cenzano, Jaime Cebolla-Cornejo and Ascensi\'on Do\~nate-Mart\'inez
Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare
14 pages, 1 figure, 1 table
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 08:37:22 GMT" } ]
1,695,168,000,000
[ [ "García-Gómez", "Juan M.", "" ], [ "Blanes-Selva", "Vicent", "" ], [ "Cenzano", "José Carlos de Bartolomé", "" ], [ "Cebolla-Cornejo", "Jaime", "" ], [ "Doñate-Martínez", "Ascensión", "" ] ]
2309.10532
Yuan Yang
Yuan Yang, Deepayan Sanyal, James Ainooson, Joel Michelson, Effat Farhana, Maithilee Kunda
A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning Using Contrastive Perceptual and Conceptual Processing
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We introduce a new neural architecture for solving visual abstract reasoning tasks inspired by human cognition, specifically by observations that human abstract reasoning often interleaves perceptual and conceptual processing as part of a flexible, iterative, and dynamic cognitive process. Inspired by this principle, our architecture models visual abstract reasoning as an iterative, self-contrasting learning process that pursues consistency between perceptual and conceptual processing of visual stimuli. We explain how this new Contrastive Perceptual-Conceptual Network (CPCNet) works using matrix reasoning problems in the style of the well-known Raven's Progressive Matrices intelligence test. Experiments on the machine learning dataset RAVEN show that CPCNet achieves higher accuracy than all previously published models while also using the weakest inductive bias. We also point out a substantial and previously unremarked class imbalance in the original RAVEN dataset, and we propose a new variant of RAVEN -- AB-RAVEN -- that is more balanced in terms of abstract concepts.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 11:18:01 GMT" }, { "version": "v2", "created": "Thu, 21 Sep 2023 01:42:46 GMT" }, { "version": "v3", "created": "Fri, 20 Oct 2023 09:02:22 GMT" } ]
1,698,019,200,000
[ [ "Yang", "Yuan", "" ], [ "Sanyal", "Deepayan", "" ], [ "Ainooson", "James", "" ], [ "Michelson", "Joel", "" ], [ "Farhana", "Effat", "" ], [ "Kunda", "Maithilee", "" ] ]
2309.10737
Tuan Dam
Tuan Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo D'Eramo, Odalric-Ambrym Maillard
Monte-Carlo tree search with uncertainty propagation via optimal transport
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) designed for highly stochastic and partially observable Markov decision processes. We adopt a probabilistic approach, modeling both value and action-value nodes as Gaussian distributions. We introduce a novel backup operator that computes value nodes as the Wasserstein barycenter of their action-value children nodes; thus, propagating the uncertainty of the estimate across the tree to the root node. We study our novel backup operator when using a novel combination of $L^1$-Wasserstein barycenter with $\alpha$-divergence, by drawing a notable connection to the generalized mean backup operator. We complement our probabilistic backup operator with two sampling strategies, based on optimistic selection and Thompson sampling, obtaining our Wasserstein MCTS algorithm. We provide theoretical guarantees of asymptotic convergence to the optimal policy, and an empirical evaluation on several stochastic and partially observable environments, where our approach outperforms well-known related baselines.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 16:32:04 GMT" } ]
1,695,168,000,000
[ [ "Dam", "Tuan", "" ], [ "Stenger", "Pascal", "" ], [ "Schneider", "Lukas", "" ], [ "Pajarinen", "Joni", "" ], [ "D'Eramo", "Carlo", "" ], [ "Maillard", "Odalric-Ambrym", "" ] ]
2309.10871
Arthur Van Der Staaij
Arthur van der Staaij, Jelmer Prins, Vincent L. Prins, Julian Poelsma, Thera Smit, Matthias M\"uller-Brockhausen, Mike Preuss
Believable Minecraft Settlements by Means of Decentralised Iterative Planning
8 pages, 8 figures, to be published in "2023 IEEE Conference on Games (CoG)"
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Procedural city generation that focuses on believability and adaptability to random terrain is a difficult challenge in the field of Procedural Content Generation (PCG). Dozens of researchers compete for a realistic approach in challenges such as the Generative Settlement Design in Minecraft (GDMC), in which our method has won the 2022 competition. This was achieved through a decentralised, iterative planning process that is transferable to similar generation processes that aims to produce "organic" content procedurally.
[ { "version": "v1", "created": "Tue, 19 Sep 2023 18:32:49 GMT" } ]
1,695,254,400,000
[ [ "van der Staaij", "Arthur", "" ], [ "Prins", "Jelmer", "" ], [ "Prins", "Vincent L.", "" ], [ "Poelsma", "Julian", "" ], [ "Smit", "Thera", "" ], [ "Müller-Brockhausen", "Matthias", "" ], [ "Preuss", "Mike", "" ] ]
2309.10982
Bingzhe Wu
Bingzhe Wu
Is GPT4 a Good Trader?
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recently, large language models (LLMs), particularly GPT-4, have demonstrated significant capabilities in various planning and reasoning tasks \cite{cheng2023gpt4,bubeck2023sparks}. Motivated by these advancements, there has been a surge of interest among researchers to harness the capabilities of GPT-4 for the automated design of quantitative factors that do not overlap with existing factor libraries, with an aspiration to achieve alpha returns \cite{webpagequant}. In contrast to these work, this study aims to examine the fidelity of GPT-4's comprehension of classic trading theories and its proficiency in applying its code interpreter abilities to real-world trading data analysis. Such an exploration is instrumental in discerning whether the underlying logic GPT-4 employs for trading is intrinsically reliable. Furthermore, given the acknowledged interpretative latitude inherent in most trading theories, we seek to distill more precise methodologies of deploying these theories from GPT-4's analytical process, potentially offering invaluable insights to human traders. To achieve this objective, we selected daily candlestick (K-line) data from specific periods for certain assets, such as the Shanghai Stock Index. Through meticulous prompt engineering, we guided GPT-4 to analyze the technical structures embedded within this data, based on specific theories like the Elliott Wave Theory. We then subjected its analytical output to manual evaluation, assessing its interpretative depth and accuracy vis-\`a-vis these trading theories from multiple dimensions. The results and findings from this study could pave the way for a synergistic amalgamation of human expertise and AI-driven insights in the realm of trading.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 00:47:52 GMT" } ]
1,695,254,400,000
[ [ "Wu", "Bingzhe", "" ] ]
2309.11064
Vipula Rawte
Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Amit Sheth, Amitava Das
Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 05:04:16 GMT" } ]
1,695,254,400,000
[ [ "Rawte", "Vipula", "" ], [ "Priya", "Prachi", "" ], [ "Tonmoy", "S. M Towhidul Islam", "" ], [ "Zaman", "S M Mehedi", "" ], [ "Sheth", "Amit", "" ], [ "Das", "Amitava", "" ] ]
2309.11155
Merel De Leeuw Den Bouter
Merel de Leeuw den Bouter, Javier Lloret Pardo, Zeno Geradts, Marcel Worring
ProtoExplorer: Interpretable Forensic Analysis of Deepfake Videos using Prototype Exploration and Refinement
15 pages, 6 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
In high-stakes settings, Machine Learning models that can provide predictions that are interpretable for humans are crucial. This is even more true with the advent of complex deep learning based models with a huge number of tunable parameters. Recently, prototype-based methods have emerged as a promising approach to make deep learning interpretable. We particularly focus on the analysis of deepfake videos in a forensics context. Although prototype-based methods have been introduced for the detection of deepfake videos, their use in real-world scenarios still presents major challenges, in that prototypes tend to be overly similar and interpretability varies between prototypes. This paper proposes a Visual Analytics process model for prototype learning, and, based on this, presents ProtoExplorer, a Visual Analytics system for the exploration and refinement of prototype-based deepfake detection models. ProtoExplorer offers tools for visualizing and temporally filtering prototype-based predictions when working with video data. It disentangles the complexity of working with spatio-temporal prototypes, facilitating their visualization. It further enables the refinement of models by interactively deleting and replacing prototypes with the aim to achieve more interpretable and less biased predictions while preserving detection accuracy. The system was designed with forensic experts and evaluated in a number of rounds based on both open-ended think aloud evaluation and interviews. These sessions have confirmed the strength of our prototype based exploration of deepfake videos while they provided the feedback needed to continuously improve the system.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 09:03:56 GMT" } ]
1,695,254,400,000
[ [ "Bouter", "Merel de Leeuw den", "" ], [ "Pardo", "Javier Lloret", "" ], [ "Geradts", "Zeno", "" ], [ "Worring", "Marcel", "" ] ]
2309.11202
Uduak Uboh
Uduak Uboh
Using Artificial Intelligence for the Automation of Knitting Patterns
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Knitting patterns are a crucial component in the creation and design of knitted materials. Traditionally, these patterns were taught informally, but thanks to advancements in technology, anyone interested in knitting can use the patterns as a guide to start knitting. Perhaps because knitting is mostly a hobby, with the exception of industrial manufacturing utilising specialised knitting machines, the use of Al in knitting is less widespread than its application in other fields. However, it is important to determine whether knitted pattern classification using an automated system is viable. In order to recognise and classify knitting patterns. Using data augmentation and a transfer learning technique, this study proposes a deep learning model. The Inception ResNet-V2 is the main feature extraction and classification algorithm used in the model. Metrics like accuracy, logarithmic loss, F1-score, precision, and recall score were used to evaluate the model. The model evaluation's findings demonstrate high model accuracy, precision, recall, and F1 score. In addition, the AUC score for majority of the classes was in the range (0.7-0.9). A comparative analysis was done using other pretrained models and a ResNet-50 model with transfer learning and the proposed model evaluation results surpassed all others. The major limitation for this project is time, as with more time, there might have been better accuracy over a larger number of epochs.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 10:38:08 GMT" } ]
1,695,254,400,000
[ [ "Uboh", "Uduak", "" ] ]
2309.11224
Nardine Osman
Nardine Osman and Bruno Rosell i Gui and Carles Sierra
Leveraging Diversity in Online Interactions
null
Workshops at the Second International Conference on Hybrid Human-Artificial Intelligence (HHAI-WS 2023), June 26-27, 2023, Munich, Germany
null
https://ceur-ws.org/Vol-3456/short5-9.pdf
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
This paper addresses the issue of connecting people online to help them find support with their day-to-day problems. We make use of declarative norms for mediating online interactions, and we specifically focus on the issue of leveraging diversity when connecting people. We run pilots at different university sites, and the results show relative success in the diversity of the selected profiles, backed by high user satisfaction.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 11:28:39 GMT" } ]
1,707,350,400,000
[ [ "Osman", "Nardine", "" ], [ "Gui", "Bruno Rosell i", "" ], [ "Sierra", "Carles", "" ] ]
2309.11231
Jonnathan Berrezueta-Guzman
Jonnathan Berrezueta-Guzman, Laura Malache-Silva and Stephan Krusche
ChatGPT-4 as a Tool for Reviewing Academic Books in Spanish
Preprint. Paper accepted in the 18\textsuperscript{th} Latin American Conference on Learning Technologies (LACLO 2023), 14 pages
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
This study evaluates the potential of ChatGPT-4, an artificial intelligence language model developed by OpenAI, as an editing tool for Spanish literary and academic books. The need for efficient and accessible reviewing and editing processes in the publishing industry has driven the search for automated solutions. ChatGPT-4, being one of the most advanced language models, offers notable capabilities in text comprehension and generation. In this study, the features and capabilities of ChatGPT-4 are analyzed in terms of grammatical correction, stylistic coherence, and linguistic enrichment of texts in Spanish. Tests were conducted with 100 literary and academic texts, where the edits made by ChatGPT-4 were compared to those made by expert human reviewers and editors. The results show that while ChatGPT-4 is capable of making grammatical and orthographic corrections with high accuracy and in a very short time, it still faces challenges in areas such as context sensitivity, bibliometric analysis, deep contextual understanding, and interaction with visual content like graphs and tables. However, it is observed that collaboration between ChatGPT-4 and human reviewers and editors can be a promising strategy for improving efficiency without compromising quality. Furthermore, the authors consider that ChatGPT-4 represents a valuable tool in the editing process, but its use should be complementary to the work of human editors to ensure high-caliber editing in Spanish literary and academic books.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 11:44:45 GMT" } ]
1,695,254,400,000
[ [ "Berrezueta-Guzman", "Jonnathan", "" ], [ "Malache-Silva", "Laura", "" ], [ "Krusche", "Stephan", "" ] ]
2309.11236
Malte Luttermann
Malte Luttermann, Tanya Braun, Ralf M\"oller, Marcel Gehrke
Colour Passing Revisited: Lifted Model Construction with Commutative Factors
Extended version of paper accepted to the Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-2024)
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes. To apply lifted inference, a lifted representation has to be obtained, and to do so, the so-called colour passing algorithm is the state of the art. The colour passing algorithm, however, is bound to a specific inference algorithm and we found that it ignores commutativity of factors while constructing a lifted representation. We contribute a modified version of the colour passing algorithm that uses logical variables to construct a lifted representation independent of a specific inference algorithm while at the same time exploiting commutativity of factors during an offline-step. Our proposed algorithm efficiently detects more symmetries than the state of the art and thereby drastically increases compression, yielding significantly faster online query times for probabilistic inference when the resulting model is applied.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 11:57:19 GMT" }, { "version": "v2", "created": "Fri, 15 Dec 2023 15:28:09 GMT" } ]
1,702,857,600,000
[ [ "Luttermann", "Malte", "" ], [ "Braun", "Tanya", "" ], [ "Möller", "Ralf", "" ], [ "Gehrke", "Marcel", "" ] ]
2309.11274
Bestoun Ahmed Dr.
Manal Rahal and Bestoun S. Ahmed and Jorgen Samuelsson
Machine Learning Data Suitability and Performance Testing Using Fault Injection Testing Framework
18 pages
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Creating resilient machine learning (ML) systems has become necessary to ensure production-ready ML systems that acquire user confidence seamlessly. The quality of the input data and the model highly influence the successful end-to-end testing in data-sensitive systems. However, the testing approaches of input data are not as systematic and are few compared to model testing. To address this gap, this paper presents the Fault Injection for Undesirable Learning in input Data (FIUL-Data) testing framework that tests the resilience of ML models to multiple intentionally-triggered data faults. Data mutators explore vulnerabilities of ML systems against the effects of different fault injections. The proposed framework is designed based on three main ideas: The mutators are not random; one data mutator is applied at an instance of time, and the selected ML models are optimized beforehand. This paper evaluates the FIUL-Data framework using data from analytical chemistry, comprising retention time measurements of anti-sense oligonucleotide. Empirical evaluation is carried out in a two-step process in which the responses of selected ML models to data mutation are analyzed individually and then compared with each other. The results show that the FIUL-Data framework allows the evaluation of the resilience of ML models. In most experiments cases, ML models show higher resilience at larger training datasets, where gradient boost performed better than support vector regression in smaller training sets. Overall, the mean squared error metric is useful in evaluating the resilience of models due to its higher sensitivity to data mutation.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 12:58:35 GMT" } ]
1,695,254,400,000
[ [ "Rahal", "Manal", "" ], [ "Ahmed", "Bestoun S.", "" ], [ "Samuelsson", "Jorgen", "" ] ]
2309.11284
Ma Qian
Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, and Wanyu Wang
Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting
9 pages, accepted by CIKM'23
null
10.1145/3583780.3614910
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With the acceleration of urbanization, traffic forecasting has become an essential role in smart city construction. In the context of spatio-temporal prediction, the key lies in how to model the dependencies of sensors. However, existing works basically only consider the micro relationships between sensors, where the sensors are treated equally, and their macroscopic dependencies are neglected. In this paper, we argue to rethink the sensor's dependency modeling from two hierarchies: regional and global perspectives. Particularly, we merge original sensors with high intra-region correlation as a region node to preserve the inter-region dependency. Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning. In pursuit of the generality and reality of node representations, we incorporate a Meta GCN to calibrate the regional and global nodes in the physical data space. Furthermore, we devise the cross-hierarchy graph convolution to propagate information from different hierarchies. In a nutshell, we propose a Hierarchical Information Enhanced Spatio-Temporal prediction method, HIEST, to create and utilize the regional dependency and common spatio-temporal patterns. Extensive experiments have verified the leading performance of our HIEST against state-of-the-art baselines. We publicize the code to ease reproducibility.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 13:08:34 GMT" } ]
1,695,254,400,000
[ [ "Ma", "Qian", "" ], [ "Zhang", "Zijian", "" ], [ "Zhao", "Xiangyu", "" ], [ "Li", "Haoliang", "" ], [ "Zhao", "Hongwei", "" ], [ "Wang", "Yiqi", "" ], [ "Liu", "Zitao", "" ], [ "Wang", "Wanyu", "" ] ]
2309.11356
Ruwan Wickramarachchi
Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth
A Comprehensive Survey on Rare Event Prediction
44 pages
null
null
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
Rare event prediction involves identifying and forecasting events with a low probability using machine learning and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the machine learning pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and machine learning. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 14:36:57 GMT" } ]
1,695,254,400,000
[ [ "Shyalika", "Chathurangi", "" ], [ "Wickramarachchi", "Ruwan", "" ], [ "Sheth", "Amit", "" ] ]
2309.11361
Yuan An
Yuan An, Jane Greenberg, Alex Kalinowski, Xintong Zhao, Xiaohua Hu, Fernando J. Uribe-Romo, Kyle Langlois, Jacob Furst, Diego A. G\'omez-Gualdr\'on
Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG) Using LLM
In 17th International Conference on Metadata and Semantics Research, October 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing the LLM, ChatGPT, to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 14:43:43 GMT" }, { "version": "v2", "created": "Thu, 6 Jun 2024 15:35:09 GMT" } ]
1,717,718,400,000
[ [ "An", "Yuan", "" ], [ "Greenberg", "Jane", "" ], [ "Kalinowski", "Alex", "" ], [ "Zhao", "Xintong", "" ], [ "Hu", "Xiaohua", "" ], [ "Uribe-Romo", "Fernando J.", "" ], [ "Langlois", "Kyle", "" ], [ "Furst", "Jacob", "" ], [ "Gómez-Gualdrón", "Diego A.", "" ] ]
2309.11469
Zhaohong Deng
Qiongdan Lou, Zhaohong Deng, Zhiyong Xiao, Kup-Sze Choi, Shitong Wang
Multi-Label Takagi-Sugeno-Kang Fuzzy System
This work has been accepted by IEEE Transactions on Fuzzy Systems
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multi-label classification can effectively identify the relevant labels of an instance from a given set of labels. However,the modeling of the relationship between the features and the labels is critical to the classification performance. To this end, we propose a new multi-label classification method, called Multi-Label Takagi-Sugeno-Kang Fuzzy System (ML-TSK FS), to improve the classification performance. The structure of ML-TSK FS is designed using fuzzy rules to model the relationship between features and labels. The fuzzy system is trained by integrating fuzzy inference based multi-label correlation learning with multi-label regression loss. The proposed ML-TSK FS is evaluated experimentally on 12 benchmark multi-label datasets. 1 The results show that the performance of ML-TSK FS is competitive with existing methods in terms of various evaluation metrics, indicating that it is able to model the feature-label relationship effectively using fuzzy inference rules and enhances the classification performance.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 17:09:09 GMT" } ]
1,695,254,400,000
[ [ "Lou", "Qiongdan", "" ], [ "Deng", "Zhaohong", "" ], [ "Xiao", "Zhiyong", "" ], [ "Choi", "Kup-Sze", "" ], [ "Wang", "Shitong", "" ] ]
2309.11473
Zhaohong Deng
Wei Zhang, Zhaohong Deng, Te Zhang, Kup-Sze Choi, Shitong Wang
Multi-view Fuzzy Representation Learning with Rules based Model
This work has been accepted by IEEE Transactions on Knowledge and Data Engineering
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Unsupervised multi-view representation learning has been extensively studied for mining multi-view data. However, some critical challenges remain. On the one hand, the existing methods cannot explore multi-view data comprehensively since they usually learn a common representation between views, given that multi-view data contains both the common information between views and the specific information within each view. On the other hand, to mine the nonlinear relationship between data, kernel or neural network methods are commonly used for multi-view representation learning. However, these methods are lacking in interpretability. To this end, this paper proposes a new multi-view fuzzy representation learning method based on the interpretable Takagi-Sugeno-Kang (TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation learning from two aspects. First, multi-view data are transformed into a high-dimensional fuzzy feature space, while the common information between views and specific information of each view are explored simultaneously. Second, a new regularization method based on L_(2,1)-norm regression is proposed to mine the consistency information between views, while the geometric structure of the data is preserved through the Laplacian graph. Finally, extensive experiments on many benchmark multi-view datasets are conducted to validate the superiority of the proposed method.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 17:13:15 GMT" } ]
1,695,254,400,000
[ [ "Zhang", "Wei", "" ], [ "Deng", "Zhaohong", "" ], [ "Zhang", "Te", "" ], [ "Choi", "Kup-Sze", "" ], [ "Wang", "Shitong", "" ] ]
2309.11478
Hanyi Wang
Yuqian Sun, Hanyi Wang, Pok Man Chan, Morteza Tabibi, Yan Zhang, Huan Lu, Yuheng Chen, Chang Hee Lee, Ali Asadipour
Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story engineering to transform fictional game characters into "live" social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC's personality and worldview, (2) Presenting Live Stories to the Community, allowing the SC to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the SC and users. We employed the LLM GPT-3 to drive our SSC prototypes, "David" and "Catherine," and evaluated their performance in an online gaming community, "DE (Alias)," on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of SCs in community settings.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 17:23:05 GMT" } ]
1,695,254,400,000
[ [ "Sun", "Yuqian", "" ], [ "Wang", "Hanyi", "" ], [ "Chan", "Pok Man", "" ], [ "Tabibi", "Morteza", "" ], [ "Zhang", "Yan", "" ], [ "Lu", "Huan", "" ], [ "Chen", "Yuheng", "" ], [ "Lee", "Chang Hee", "" ], [ "Asadipour", "Ali", "" ] ]
2309.11528
Hanzhu Chen
Jie Wang, Hanzhu Chen, Qitan Lv, Zhihao Shi, Jiajun Chen, Huarui He, Hongtao Xie, Yongdong Zhang, and Feng Wu
Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction
arXiv admin note: text overlap with arXiv:2103.03642
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Inductive link prediction -- where entities during training and inference stages can be different -- has shown great potential for completing evolving knowledge graphs in an entity-independent manner. Many popular methods mainly focus on modeling graph-level features, while the edge-level interactions -- especially the semantic correlations between relations -- have been less explored. However, we notice a desirable property of semantic correlations between relations is that they are inherently edge-level and entity-independent. This implies the great potential of the semantic correlations for the entity-independent inductive link prediction task. Inspired by this observation, we propose a novel subgraph-based method, namely TACO, to model Topology-Aware COrrelations between relations that are highly correlated to their topological structures within subgraphs. Specifically, we prove that semantic correlations between any two relations can be categorized into seven topological patterns, and then proposes Relational Correlation Network (RCN) to learn the importance of each pattern. To further exploit the potential of RCN, we propose Complete Common Neighbor induced subgraph that can effectively preserve complete topological patterns within the subgraph. Extensive experiments demonstrate that TACO effectively unifies the graph-level information and edge-level interactions to jointly perform reasoning, leading to a superior performance over existing state-of-the-art methods for the inductive link prediction task.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 08:11:58 GMT" }, { "version": "v2", "created": "Sun, 24 Mar 2024 10:27:10 GMT" } ]
1,711,411,200,000
[ [ "Wang", "Jie", "" ], [ "Chen", "Hanzhu", "" ], [ "Lv", "Qitan", "" ], [ "Shi", "Zhihao", "" ], [ "Chen", "Jiajun", "" ], [ "He", "Huarui", "" ], [ "Xie", "Hongtao", "" ], [ "Zhang", "Yongdong", "" ], [ "Wu", "Feng", "" ] ]
2309.11608
Ryan Turner
Daniel Kharitonov and Ryan Turner
Dataset Factory: A Toolchain For Generative Computer Vision Datasets
Presented at the datacomp.ai workshop at ICCV 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Generative AI workflows heavily rely on data-centric tasks - such as filtering samples by annotation fields, vector distances, or scores produced by custom classifiers. At the same time, computer vision datasets are quickly approaching petabyte volumes, rendering data wrangling difficult. In addition, the iterative nature of data preparation necessitates robust dataset sharing and versioning mechanisms, both of which are hard to implement ad-hoc. To solve these challenges, we propose a "dataset factory" approach that separates the storage and processing of samples from metadata and enables data-centric operations at scale for machine learning teams and individual researchers.
[ { "version": "v1", "created": "Wed, 20 Sep 2023 19:43:37 GMT" } ]
1,695,340,800,000
[ [ "Kharitonov", "Daniel", "" ], [ "Turner", "Ryan", "" ] ]
2309.11724
Rui Liu
Rui Liu, Bin Liu, Haizhou Li
Emotion-Aware Prosodic Phrasing for Expressive Text-to-Speech
Submitted to ICASSP'2024
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Prosodic phrasing is crucial to the naturalness and intelligibility of end-to-end Text-to-Speech (TTS). There exist both linguistic and emotional prosody in natural speech. As the study of prosodic phrasing has been linguistically motivated, prosodic phrasing for expressive emotion rendering has not been well studied. In this paper, we propose an emotion-aware prosodic phrasing model, termed \textit{EmoPP}, to mine the emotional cues of utterance accurately and predict appropriate phrase breaks. We first conduct objective observations on the ESD dataset to validate the strong correlation between emotion and prosodic phrasing. Then the objective and subjective evaluations show that the EmoPP outperforms all baselines and achieves remarkable performance in terms of emotion expressiveness. The audio samples and the code are available at \url{https://github.com/AI-S2-Lab/EmoPP}.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 01:51:10 GMT" } ]
1,695,340,800,000
[ [ "Liu", "Rui", "" ], [ "Liu", "Bin", "" ], [ "Li", "Haizhou", "" ] ]
2309.11737
Zhaoyi Hou
Zhaoyi Joey Hou, Li Zhang, Chris Callison-Burch
Choice-75: A Dataset on Decision Branching in Script Learning
To be published in LREC-COLING-2024
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Script learning studies how stereotypical events unfold, enabling machines to reason about narratives with implicit information. Previous works mostly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people's circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performance, there is still notable headroom in hard scenarios.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 02:23:44 GMT" }, { "version": "v2", "created": "Mon, 18 Mar 2024 01:35:48 GMT" } ]
1,710,806,400,000
[ [ "Hou", "Zhaoyi Joey", "" ], [ "Zhang", "Li", "" ], [ "Callison-Burch", "Chris", "" ] ]
2309.11753
Zhourui Guo
Zhourui Guo, Meng Yao, Yang Yu, Qiyue Yin
Improve the efficiency of deep reinforcement learning through semantic exploration guided by natural language
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Reinforcement learning is a powerful technique for learning from trial and error, but it often requires a large number of interactions to achieve good performance. In some domains, such as sparse-reward tasks, an oracle that can provide useful feedback or guidance to the agent during the learning process is really of great importance. However, querying the oracle too frequently may be costly or impractical, and the oracle may not always have a clear answer for every situation. Therefore, we propose a novel method for interacting with the oracle in a selective and efficient way, using a retrieval-based approach. We assume that the interaction can be modeled as a sequence of templated questions and answers, and that there is a large corpus of previous interactions available. We use a neural network to encode the current state of the agent and the oracle, and retrieve the most relevant question from the corpus to ask the oracle. We then use the oracle's answer to update the agent's policy and value function. We evaluate our method on an object manipulation task. We show that our method can significantly improve the efficiency of RL by reducing the number of interactions needed to reach a certain level of performance, compared to baselines that do not use the oracle or use it in a naive way.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 03:25:35 GMT" } ]
1,695,340,800,000
[ [ "Guo", "Zhourui", "" ], [ "Yao", "Meng", "" ], [ "Yu", "Yang", "" ], [ "Yin", "Qiyue", "" ] ]
2309.11805
Preetam Ghosh
Preetam Ghosh, Vaishali Sadaphal
JobRecoGPT -- Explainable job recommendations using LLMs
10 pages, 29 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
In today's rapidly evolving job market, finding the right opportunity can be a daunting challenge. With advancements in the field of AI, computers can now recommend suitable jobs to candidates. However, the task of recommending jobs is not same as recommending movies to viewers. Apart from must-have criteria, like skills and experience, there are many subtle aspects to a job which can decide if it is a good fit or not for a given candidate. Traditional approaches can capture the quantifiable aspects of jobs and candidates, but a substantial portion of the data that is present in unstructured form in the job descriptions and resumes is lost in the process of conversion to structured format. As of late, Large Language Models (LLMs) have taken over the AI field by storm with extraordinary performance in fields where text-based data is available. Inspired by the superior performance of LLMs, we leverage their capability to understand natural language for capturing the information that was previously getting lost during the conversion of unstructured data to structured form. To this end, we compare performance of four different approaches for job recommendations namely, (i) Content based deterministic, (ii) LLM guided, (iii) LLM unguided, and (iv) Hybrid. In this study, we present advantages and limitations of each method and evaluate their performance in terms of time requirements.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 06:25:28 GMT" } ]
1,695,340,800,000
[ [ "Ghosh", "Preetam", "" ], [ "Sadaphal", "Vaishali", "" ] ]
2309.11907
Haoyu Wang
Haoyu Wang, Xin Yuan, Qinqing Ren
Learning to Recover for Safe Reinforcement Learning
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Safety controllers is widely used to achieve safe reinforcement learning. Most methods that apply a safety controller are using handcrafted safety constraints to construct the safety controller. However, when the environment dynamics are sophisticated, handcrafted safety constraints become unavailable. Therefore, it worth to research on constructing safety controllers by learning algorithms. We propose a three-stage architecture for safe reinforcement learning, namely TU-Recovery Architecture. A safety critic and a recovery policy is learned before task training. They form a safety controller to ensure safety in task training. Then a phenomenon induced by disagreement between task policy and recovery policy, called adversarial phenomenon, which reduces learning efficiency and model performance, is described. Auxiliary reward is proposed to mitigate adversarial phenomenon, while help the task policy to learn to recover from high-risk states. A series of experiments are conducted in a robot navigation environment. Experiments demonstrate that TU-Recovery outperforms unconstrained counterpart in both reward gaining and constraint violations during task training, and auxiliary reward further improve TU-Recovery in reward-to-cost ratio by significantly reduce constraint violations.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 09:17:38 GMT" } ]
1,695,340,800,000
[ [ "Wang", "Haoyu", "" ], [ "Yuan", "Xin", "" ], [ "Ren", "Qinqing", "" ] ]
2309.11937
Helena L\"ofstr\"om HeLo
Helena L\"ofstr\"om
On the Definition of Appropriate Trust and the Tools that Come with it
8 pages, 3 figures, Conference: ICDATA 2023
2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)
10.1109/CSCE60160.2023.00256
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Evaluating the efficiency of human-AI interactions is challenging, including subjective and objective quality aspects. With the focus on the human experience of the explanations, evaluations of explanation methods have become mostly subjective, making comparative evaluations almost impossible and highly linked to the individual user. However, it is commonly agreed that one aspect of explanation quality is how effectively the user can detect if the predictions are trustworthy and correct, i.e., if the explanations can increase the user's appropriate trust in the model. This paper starts with the definitions of appropriate trust from the literature. It compares the definitions with model performance evaluation, showing the strong similarities between appropriate trust and model performance evaluation. The paper's main contribution is a novel approach to evaluating appropriate trust by taking advantage of the likenesses between definitions. The paper offers several straightforward evaluation methods for different aspects of user performance, including suggesting a method for measuring uncertainty and appropriate trust in regression.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 09:52:06 GMT" } ]
1,715,299,200,000
[ [ "Löfström", "Helena", "" ] ]
2309.11975
John Burden
John Burden, Konstantinos Voudouris, Ryan Burnell, Danaja Rutar, Lucy Cheke, Jos\'e Hern\'andez-Orallo
Inferring Capabilities from Task Performance with Bayesian Triangulation
8 Pages + 14 pages of Appendices. 15 Figures. Submitted to AAAI 2024. Preprint
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As machine learning models become more general, we need to characterise them in richer, more meaningful ways. We describe a method to infer the cognitive profile of a system from diverse experimental data. To do so, we introduce measurement layouts that model how task-instance features interact with system capabilities to affect performance. These features must be triangulated in complex ways to be able to infer capabilities from non-populational data -- a challenge for traditional psychometric and inferential tools. Using the Bayesian probabilistic programming library PyMC, we infer different cognitive profiles for agents in two scenarios: 68 actual contestants in the AnimalAI Olympics and 30 synthetic agents for O-PIAAGETS, an object permanence battery. We showcase the potential for capability-oriented evaluation.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 11:19:26 GMT" } ]
1,695,340,800,000
[ [ "Burden", "John", "" ], [ "Voudouris", "Konstantinos", "" ], [ "Burnell", "Ryan", "" ], [ "Rutar", "Danaja", "" ], [ "Cheke", "Lucy", "" ], [ "Hernández-Orallo", "José", "" ] ]
2309.12113
Feng Li
Feng Li, Yuqi Chai, Huan Yang, Pengfei Hu, Lingjie Duan
Incentivizing Massive Unknown Workers for Budget-Limited Crowdsensing: From Off-Line and On-Line Perspectives
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
How to incentivize strategic workers using limited budget is a very fundamental problem for crowdsensing systems; nevertheless, since the sensing abilities of the workers may not always be known as prior knowledge due to the diversities of their sensor devices and behaviors, it is difficult to properly select and pay the unknown workers. Although the uncertainties of the workers can be addressed by the standard Combinatorial Multi-Armed Bandit (CMAB) framework in existing proposals through a trade-off between exploration and exploitation, we may not have sufficient budget to enable the trade-off among the individual workers, especially when the number of the workers is huge while the budget is limited. Moreover, the standard CMAB usually assumes the workers always stay in the system, whereas the workers may join in or depart from the system over time, such that what we have learnt for an individual worker cannot be applied after the worker leaves. To address the above challenging issues, in this paper, we first propose an off-line Context-Aware CMAB-based Incentive (CACI) mechanism. We innovate in leveraging the exploration-exploitation trade-off in an elaborately partitioned context space instead of the individual workers, to effectively incentivize the massive unknown workers with a very limited budget. We also extend the above basic idea to the on-line setting where unknown workers may join in or depart from the systems dynamically, and propose an on-line version of the CACI mechanism. We perform rigorous theoretical analysis to reveal the upper bounds on the regrets of our CACI mechanisms and to prove their truthfulness and individual rationality, respectively. Extensive experiments on both synthetic and real datasets are also conducted to verify the efficacy of our mechanisms.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 14:30:42 GMT" }, { "version": "v2", "created": "Wed, 3 Jan 2024 02:53:30 GMT" } ]
1,704,326,400,000
[ [ "Li", "Feng", "" ], [ "Chai", "Yuqi", "" ], [ "Yang", "Huan", "" ], [ "Hu", "Pengfei", "" ], [ "Duan", "Lingjie", "" ] ]
2309.12132
Chunmo Zheng
Chunmo Zheng, Saika Wong, Xing Su, Yinqiu Tang
A knowledge representation approach for construction contract knowledge modeling
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The emergence of large language models (LLMs) presents an unprecedented opportunity to automate construction contract management, reducing human errors and saving significant time and costs. However, LLMs may produce convincing yet inaccurate and misleading content due to a lack of domain expertise. To address this issue, expert-driven contract knowledge can be represented in a structured manner to constrain the automatic contract management process. This paper introduces the Nested Contract Knowledge Graph (NCKG), a knowledge representation approach that captures the complexity of contract knowledge using a nested structure. It includes a nested knowledge representation framework, a NCKG ontology built on the framework, and an implementation method. Furthermore, we present the LLM-assisted contract review pipeline enhanced with external knowledge in NCKG. Our pipeline achieves a promising performance in contract risk reviewing, shedding light on the combination of LLM and KG towards more reliable and interpretable contract management.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 14:53:36 GMT" } ]
1,695,340,800,000
[ [ "Zheng", "Chunmo", "" ], [ "Wong", "Saika", "" ], [ "Su", "Xing", "" ], [ "Tang", "Yinqiu", "" ] ]
2309.12177
Senthil Kumar Jagatheesaperumal Dr.
Roohallah Alizadehsani, Solomon Sunday Oyelere, Sadiq Hussain, Rene Ripardo Calixto, Victor Hugo C. de Albuquerque, Mohamad Roshanzamir, Mohamed Rahouti, and Senthil Kumar Jagatheesaperumal
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
13 pages, 3 figures
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. The aim of this review article is to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 15:36:06 GMT" }, { "version": "v2", "created": "Thu, 2 Nov 2023 11:06:27 GMT" } ]
1,698,969,600,000
[ [ "Alizadehsani", "Roohallah", "" ], [ "Oyelere", "Solomon Sunday", "" ], [ "Hussain", "Sadiq", "" ], [ "Calixto", "Rene Ripardo", "" ], [ "de Albuquerque", "Victor Hugo C.", "" ], [ "Roshanzamir", "Mohamad", "" ], [ "Rahouti", "Mohamed", "" ], [ "Jagatheesaperumal", "Senthil Kumar", "" ] ]
2309.12423
Oktie Hassanzadeh
Sola Shirai, Debarun Bhattacharjya, Oktie Hassanzadeh
Event Prediction using Case-Based Reasoning over Knowledge Graphs
published at WWW '23: Proceedings of the ACM Web Conference 2023. Code base: https://github.com/solashirai/WWW-EvCBR
null
10.1145/3543507.3583201
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
Applying link prediction (LP) methods over knowledge graphs (KG) for tasks such as causal event prediction presents an exciting opportunity. However, typical LP models are ill-suited for this task as they are incapable of performing inductive link prediction for new, unseen event entities and they require retraining as knowledge is added or changed in the underlying KG. We introduce a case-based reasoning model, EvCBR, to predict properties about new consequent events based on similar cause-effect events present in the KG. EvCBR uses statistical measures to identify similar events and performs path-based predictions, requiring no training step. To generalize our methods beyond the domain of event prediction, we frame our task as a 2-hop LP task, where the first hop is a causal relation connecting a cause event to a new effect event and the second hop is a property about the new event which we wish to predict. The effectiveness of our method is demonstrated using a novel dataset of newsworthy events with causal relations curated from Wikidata, where EvCBR outperforms baselines including translational-distance-based, GNN-based, and rule-based LP models.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 18:46:29 GMT" } ]
1,695,600,000,000
[ [ "Shirai", "Sola", "" ], [ "Bhattacharjya", "Debarun", "" ], [ "Hassanzadeh", "Oktie", "" ] ]
2309.12529
Shuang Ao
Shuang Ao, Tianyi Zhou, Guodong Long, Xuan Song, Jing Jiang
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes. In contrast, current reinforcement learning (RL) studies mainly focus on training an agent with a fixed morphology (e.g., skeletal structure and joint attributes) in a fixed environment, which can hardly generalize to changing environments or new tasks. In this paper, we optimize an RL agent and its morphology through ``morphology-environment co-evolution (MECE)'', in which the morphology keeps being updated to adapt to the changing environment, while the environment is modified progressively to bring new challenges and stimulate the improvement of the morphology. This leads to a curriculum to train generalizable RL, whose morphology and policy are optimized for different environments. Instead of hand-crafting the curriculum, we train two policies to automatically change the morphology and the environment. To this end, (1) we develop two novel and effective rewards for the two policies, which are solely based on the learning dynamics of the RL agent; (2) we design a scheduler to automatically determine when to change the environment and the morphology. In experiments on two classes of tasks, the morphology and RL policies trained via MECE exhibit significantly better generalization performance in unseen test environments than SOTA morphology optimization methods. Our ablation studies on the two MECE policies further show that the co-evolution between the morphology and environment is the key to the success.
[ { "version": "v1", "created": "Thu, 21 Sep 2023 22:58:59 GMT" } ]
1,695,600,000,000
[ [ "Ao", "Shuang", "" ], [ "Zhou", "Tianyi", "" ], [ "Long", "Guodong", "" ], [ "Song", "Xuan", "" ], [ "Jiang", "Jing", "" ] ]
2309.12579
Parag Saxena
Parag Saxena
From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Data-driven insights are essential for modern agriculture. This research paper introduces a machine learning framework designed to improve how we educate and reach out to people in the field of horticulture. The framework relies on data from the Horticulture Online Help Desk (HOHD), which is like a big collection of questions from people who love gardening and are part of the Extension Master Gardener Program (EMGP). This framework has two main parts. First, it uses special computer programs (machine learning models) to sort questions into categories. This helps us quickly send each question to the right expert, so we can answer it faster. Second, it looks at when questions are asked and uses that information to guess how many questions we might get in the future and what they will be about. This helps us plan on topics that will be really important. It's like knowing what questions will be popular in the coming months. We also take into account where the questions come from by looking at the Zip Code. This helps us make research that fits the challenges faced by gardeners in different places. In this paper, we demonstrate the potential of machine learning techniques to predict trends in horticulture by analyzing textual queries from homeowners. We show that NLP, classification, and time series analysis can be used to identify patterns in homeowners' queries and predict future trends in horticulture. Our results suggest that machine learning could be used to predict trends in other agricultural sectors as well. If large-scale agriculture industries curate and maintain a comparable repository of textual data, the potential for trend prediction and strategic agricultural planning could be revolutionized. This convergence of technology and agriculture offers a promising pathway for the future of sustainable farming and data-informed agricultural practices
[ { "version": "v1", "created": "Fri, 22 Sep 2023 02:15:12 GMT" } ]
1,695,600,000,000
[ [ "Saxena", "Parag", "" ] ]
2309.12655
Paolo Liberatore
Paolo Liberatore
Natural revision is contingently-conditionalized revision
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Natural revision seems so natural: it changes beliefs as little as possible to incorporate new information. Yet, some counterexamples show it wrong. It is so conservative that it never fully believes. It only believes in the current conditions. This is right in some cases and wrong in others. Which is which? The answer requires extending natural revision from simple formulae expressing universal truths (something holds) to conditionals expressing conditional truth (something holds in certain conditions). The extension is based on the basic principles natural revision follows, identified as minimal change, indifference and naivety: change beliefs as little as possible; equate the likeliness of scenarios by default; believe all until contradicted. The extension says that natural revision restricts changes to the current conditions. A comparison with an unrestricting revision shows what exactly the current conditions are. It is not what currently considered true if it contradicts the new information. It includes something more and more unlikely until the new information is at least possible.
[ { "version": "v1", "created": "Fri, 22 Sep 2023 06:52:30 GMT" } ]
1,695,600,000,000
[ [ "Liberatore", "Paolo", "" ] ]
2309.12696
Yun Qu
Jianzhun Shao, Yun Qu, Chen Chen, Hongchang Zhang, Xiangyang Ji
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
37th Conference on Neural Information Processing Systems (NeurIPS 2023)
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Offline multi-agent reinforcement learning is challenging due to the coupling effect of both distribution shift issue common in offline setting and the high dimension issue common in multi-agent setting, making the action out-of-distribution (OOD) and value overestimation phenomenon excessively severe. Tomitigate this problem, we propose a novel multi-agent offline RL algorithm, named CounterFactual Conservative Q-Learning (CFCQL) to conduct conservative value estimation. Rather than regarding all the agents as a high dimensional single one and directly applying single agent methods to it, CFCQL calculates conservative regularization for each agent separately in a counterfactual way and then linearly combines them to realize an overall conservative value estimation. We prove that it still enjoys the underestimation property and the performance guarantee as those single agent conservative methods do, but the induced regularization and safe policy improvement bound are independent of the agent number, which is therefore theoretically superior to the direct treatment referred to above, especially when the agent number is large. We further conduct experiments on four environments including both discrete and continuous action settings on both existing and our man-made datasets, demonstrating that CFCQL outperforms existing methods on most datasets and even with a remarkable margin on some of them.
[ { "version": "v1", "created": "Fri, 22 Sep 2023 08:10:25 GMT" } ]
1,695,600,000,000
[ [ "Shao", "Jianzhun", "" ], [ "Qu", "Yun", "" ], [ "Chen", "Chen", "" ], [ "Zhang", "Hongchang", "" ], [ "Ji", "Xiangyang", "" ] ]
2309.12711
Tristan Cazenave
Marc Pierre and Quentin Cohen-Solal and Tristan Cazenave
The Mathematical Game
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Monte Carlo Tree Search can be used for automated theorem proving. Holophrasm is a neural theorem prover using MCTS combined with neural networks for the policy and the evaluation. In this paper we propose to improve the performance of the Holophrasm theorem prover using other game tree search algorithms.
[ { "version": "v1", "created": "Fri, 22 Sep 2023 08:43:57 GMT" } ]
1,695,600,000,000
[ [ "Pierre", "Marc", "" ], [ "Cohen-Solal", "Quentin", "" ], [ "Cazenave", "Tristan", "" ] ]
2309.12731
Dave Raggett
Dave Raggett
Defeasible Reasoning with Knowledge Graphs
Accepted for: Knowledge Graph and Semantic Web Conference (KGSWC-2023), 13-15 September, 2023, Zaragoza, Spain
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Human knowledge is subject to uncertainties, imprecision, incompleteness and inconsistencies. Moreover, the meaning of many everyday terms is dependent on the context. That poses a huge challenge for the Semantic Web. This paper introduces work on an intuitive notation and model for defeasible reasoning with imperfect knowledge, and relates it to previous work on argumentation theory. PKN is to N3 as defeasible reasoning is to deductive logic. Further work is needed on an intuitive syntax for describing reasoning strategies and tactics in declarative terms, drawing upon the AIF ontology for inspiration. The paper closes with observations on symbolic approaches in the era of large language models.
[ { "version": "v1", "created": "Fri, 22 Sep 2023 09:27:26 GMT" } ]
1,695,600,000,000
[ [ "Raggett", "Dave", "" ] ]
2309.13218
Pivithuru Thejan Amarasinghe
Pivithuru Thejan Amarasinghe, Su Nguyen, Yuan Sun and Damminda Alahakoon
AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Business optimisation refers to the process of finding and implementing efficient and cost-effective means of operation to bring a competitive advantage for businesses. Synthesizing problem formulations is an integral part of business optimisation, which relies on human expertise to construct problem formulations using optimisation languages. Interestingly, with advancements in Large Language Models (LLMs), the human expertise needed in problem formulation can be minimized. However, developing an LLM for problem formulation is challenging, due to training data, token limitations, and lack of appropriate performance metrics. For the requirement of training data, recent attention has been directed towards fine-tuning pre-trained LLMs for downstream tasks rather than training an LLM from scratch for a specific task. In this paper, we adopt an LLM fine-tuning approach and propose an AI-Copilot for business optimisation problem formulation. For token limitations, we introduce modularization and prompt engineering techniques to synthesize complex problem formulations as modules that fit into the token limits of LLMs. Additionally, we design performance evaluation metrics that are better suited for assessing the accuracy and quality of problem formulations. The experiment results demonstrate that with this approach we can synthesize complex and large problem formulations for a typical business optimisation problem in production scheduling.
[ { "version": "v1", "created": "Fri, 22 Sep 2023 23:45:21 GMT" }, { "version": "v2", "created": "Fri, 29 Sep 2023 04:19:39 GMT" }, { "version": "v3", "created": "Wed, 18 Oct 2023 23:34:15 GMT" } ]
1,697,760,000,000
[ [ "Amarasinghe", "Pivithuru Thejan", "" ], [ "Nguyen", "Su", "" ], [ "Sun", "Yuan", "" ], [ "Alahakoon", "Damminda", "" ] ]
2309.13229
Jiaqi Wen
Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes' features and feature differences. Our empirical analysis builds DT-CNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. We also investigate their respective disaster resilience to an epidemic outbreak starting from the most popular node. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure and the speed of the epidemic outbreak, requiring various mitigation policies targeted at different people.
[ { "version": "v1", "created": "Sat, 23 Sep 2023 01:40:56 GMT" } ]
1,695,686,400,000
[ [ "Wen", "Jiaqi", "" ], [ "Gabrys", "Bogdan", "" ], [ "Musial", "Katarzyna", "" ] ]
2309.13834
Ruilin Luo
Jiayi Li, Ruilin Luo, Jiaqi Sun, Jing Xiao, Yujiu Yang
Prior Bilinear Based Models for Knowledge Graph Completion
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Bilinear based models are powerful and widely used approaches for Knowledge Graphs Completion (KGC). Although bilinear based models have achieved significant advances, these studies mainly concentrate on posterior properties (based on evidence, e.g. symmetry pattern) while neglecting the prior properties. In this paper, we find a prior property named "the law of identity" that cannot be captured by bilinear based models, which hinders them from comprehensively modeling the characteristics of KGs. To address this issue, we introduce a solution called Unit Ball Bilinear Model (UniBi). This model not only achieves theoretical superiority but also offers enhanced interpretability and performance by minimizing ineffective learning through minimal constraints. Experiments demonstrate that UniBi models the prior property and verify its interpretability and performance.
[ { "version": "v1", "created": "Mon, 25 Sep 2023 02:44:33 GMT" } ]
1,695,686,400,000
[ [ "Li", "Jiayi", "" ], [ "Luo", "Ruilin", "" ], [ "Sun", "Jiaqi", "" ], [ "Xiao", "Jing", "" ], [ "Yang", "Yujiu", "" ] ]
2309.13939
Irene Celino
Irene Celino and Heiko Paulheim
The Time Traveler's Guide to Semantic Web Research: Analyzing Fictitious Research Themes in the ESWC "Next 20 Years" Track
13 pages, 8 figures, 2 tables
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
What will Semantic Web research focus on in 20 years from now? We asked this question to the community and collected their visions in the "Next 20 years" track of ESWC 2023. We challenged the participants to submit "future" research papers, as if they were submitting to the 2043 edition of the conference. The submissions - entirely fictitious - were expected to be full scientific papers, with research questions, state of the art references, experimental results and future work, with the goal to get an idea of the research agenda for the late 2040s and early 2050s. We received ten submissions, eight of which were accepted for presentation at the conference, that mixed serious ideas of potential future research themes and discussion topics with some fun and irony. In this paper, we intend to provide a survey of those "science fiction" papers, considering the emerging research themes and topics, analysing the research methods applied by the authors in these very special submissions, and investigating also the most fictitious parts (e.g., neologisms, fabricated references). Our goal is twofold: on the one hand, we investigate what this special track tells us about the Semantic Web community and, on the other hand, we aim at getting some insights on future research practices and directions.
[ { "version": "v1", "created": "Mon, 25 Sep 2023 08:20:06 GMT" } ]
1,695,686,400,000
[ [ "Celino", "Irene", "" ], [ "Paulheim", "Heiko", "" ] ]
2309.14663
Pranav Rajbhandari
Pranav Rajbhandari, Donald Sofge
Learning Emergent Behavior in Robot Swarms with NEAT
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. However, the task of learning an individual policy to produce a desired emergent behavior remains a challenging and largely unsolved problem. We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in animals, we use an evolutionary algorithm to train a 'population' of individual behaviors to approximate a desired group behavior. We perform experiments using simulations of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics platforms conducted in the CoppeliaSim simulator. Additionally, we test on simulations of Anki Vector robots to display our algorithm's effectiveness on various modes of actuation. We evaluate our algorithm on various tasks where a somewhat complex group behavior is required for success. These tasks include an Area Coverage task, a Surround Target task, and a Wall Climb task. We compare behaviors evolved using our algorithm against 'designed policies', which we create in order to exhibit the emergent behaviors we desire.
[ { "version": "v1", "created": "Tue, 26 Sep 2023 04:40:52 GMT" } ]
1,695,772,800,000
[ [ "Rajbhandari", "Pranav", "" ], [ "Sofge", "Donald", "" ] ]
2309.14718
Andrew Fuchs
Andrew Fuchs, Andrea Passarella, Marco Conti
Optimizing delegation between human and AI collaborative agents
This work has been accepted to the 'Towards Hybrid Human-Machine Learning and Decision Making (HLDM)' workshop at ECML PKDD 2023
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team.
[ { "version": "v1", "created": "Tue, 26 Sep 2023 07:23:26 GMT" }, { "version": "v2", "created": "Wed, 11 Oct 2023 07:28:04 GMT" } ]
1,697,068,800,000
[ [ "Fuchs", "Andrew", "" ], [ "Passarella", "Andrea", "" ], [ "Conti", "Marco", "" ] ]
2309.14796
Eunseong Choi
Yoonjin Im, Eunseong Choi, Heejin Kook, Jongwuk Lee
Forgetting-aware Linear Bias for Attentive Knowledge Tracing
In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), 5 pages, 3 figures, 2 tables
null
10.1145/3583780.3615191
null
cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Knowledge Tracing (KT) aims to track proficiency based on a question-solving history, allowing us to offer a streamlined curriculum. Recent studies actively utilize attention-based mechanisms to capture the correlation between questions and combine it with the learner's characteristics for responses. However, our empirical study shows that existing attention-based KT models neglect the learner's forgetting behavior, especially as the interaction history becomes longer. This problem arises from the bias that overprioritizes the correlation of questions while inadvertently ignoring the impact of forgetting behavior. This paper proposes a simple-yet-effective solution, namely Forgetting-aware Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite its simplicity, FoLiBi is readily equipped with existing attentive KT models by effectively decomposing question correlations with forgetting behavior. FoLiBi plugged with several KT models yields a consistent improvement of up to 2.58% in AUC over state-of-the-art KT models on four benchmark datasets.
[ { "version": "v1", "created": "Tue, 26 Sep 2023 09:48:30 GMT" } ]
1,695,772,800,000
[ [ "Im", "Yoonjin", "" ], [ "Choi", "Eunseong", "" ], [ "Kook", "Heejin", "" ], [ "Lee", "Jongwuk", "" ] ]
2309.15242
Yi Wang
Yi Wang, Jieliang Luo, Adam Gaier, Evan Atherton, Hilmar Koch
PlotMap: Automated Layout Design for Building Game Worlds
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
World-building, the process of developing both the narrative and physical world of a game, plays a vital role in the game's experience. Critically acclaimed independent and AAA video games are praised for strong world building, with game maps that masterfully intertwine with and elevate the narrative, captivating players and leaving a lasting impression. However, designing game maps that support a desired narrative is challenging, as it requires satisfying complex constraints from various considerations. Most existing map generation methods focus on considerations about gameplay mechanics or map topography, while the need to support the story is typically neglected. As a result, extensive manual adjustment is still required to design a game world that facilitates particular stories. In this work, we approach this problem by introducing an extra layer of plot facility layout design that is independent of the underlying map generation method in a world-building pipeline. Concretely, we present a system that leverages Reinforcement Learning (RL) to automatically assign concrete locations on a game map to abstract locations mentioned in a given story (plot facilities), following spatial constraints derived from the story. A decision-making agent moves the plot facilities around, considering their relationship to the map and each other, to locations on the map that best satisfy the constraints of the story. Our system considers input from multiple modalities: map images as pixels, facility locations as real values, and story constraints expressed in natural language. We develop a method of generating datasets of facility layout tasks, create an RL environment to train and evaluate RL models, and further analyze the behaviors of the agents through a group of comprehensive experiments and ablation studies, aiming to provide insights for RL-based plot facility layout design.
[ { "version": "v1", "created": "Tue, 26 Sep 2023 20:13:10 GMT" } ]
1,695,859,200,000
[ [ "Wang", "Yi", "" ], [ "Luo", "Jieliang", "" ], [ "Gaier", "Adam", "" ], [ "Atherton", "Evan", "" ], [ "Koch", "Hilmar", "" ] ]
2309.15484
Kuo-Hao Ho
Kuo-Hao Ho, Ping-Chun Hsieh, Chiu-Chou Lin, You-Ren Luo, Feng-Jian Wang, I-Chen Wu
Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we propose a new approach called Adaptive Behavioral Costs in Reinforcement Learning (ABC-RL) for training a human-like agent with competitive strength. While deep reinforcement learning agents have recently achieved superhuman performance in various video games, some of these unconstrained agents may exhibit actions, such as shaking and spinning, that are not typically observed in human behavior, resulting in peculiar gameplay experiences. To behave like humans and retain similar performance, ABC-RL augments behavioral limitations as cost signals in reinforcement learning with dynamically adjusted weights. Unlike traditional constrained policy optimization, we propose a new formulation that minimizes the behavioral costs subject to a constraint of the value function. By leveraging the augmented Lagrangian, our approach is an approximation of the Lagrangian adjustment, which handles the trade-off between the performance and the human-like behavior. Through experiments conducted on 3D games in DMLab-30 and Unity ML-Agents Toolkit, we demonstrate that ABC-RL achieves the same performance level while significantly reducing instances of shaking and spinning. These findings underscore the effectiveness of our proposed approach in promoting more natural and human-like behavior during gameplay.
[ { "version": "v1", "created": "Wed, 27 Sep 2023 08:28:59 GMT" } ]
1,695,859,200,000
[ [ "Ho", "Kuo-Hao", "" ], [ "Hsieh", "Ping-Chun", "" ], [ "Lin", "Chiu-Chou", "" ], [ "Luo", "You-Ren", "" ], [ "Wang", "Feng-Jian", "" ], [ "Wu", "I-Chen", "" ] ]
2309.15517
Kuo-Hao Ho
Kuo-Hao Ho, Ruei-Yu Jheng, Ji-Han Wu, Fan Chiang, Yen-Chi Chen, Yuan-Yu Wu, I-Chen Wu
Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Job-shop scheduling problem (JSP) is a mathematical optimization problem widely used in industries like manufacturing, and flexible JSP (FJSP) is also a common variant. Since they are NP-hard, it is intractable to find the optimal solution for all cases within reasonable times. Thus, it becomes important to develop efficient heuristics to solve JSP/FJSP. A kind of method of solving scheduling problems is construction heuristics, which constructs scheduling solutions via heuristics. Recently, many methods for construction heuristics leverage deep reinforcement learning (DRL) with graph neural networks (GNN). In this paper, we propose a new approach, named residual scheduling, to solving JSP/FJSP. In this new approach, we remove irrelevant machines and jobs such as those finished, such that the states include the remaining (or relevant) machines and jobs only. Our experiments show that our approach reaches state-of-the-art (SOTA) among all known construction heuristics on most well-known open JSP and FJSP benchmarks. In addition, we also observe that even though our model is trained for scheduling problems of smaller sizes, our method still performs well for scheduling problems of large sizes. Interestingly in our experiments, our approach even reaches zero gap for 49 among 50 JSP instances whose job numbers are more than 150 on 20 machines.
[ { "version": "v1", "created": "Wed, 27 Sep 2023 09:33:56 GMT" }, { "version": "v2", "created": "Tue, 3 Oct 2023 01:28:16 GMT" } ]
1,696,377,600,000
[ [ "Ho", "Kuo-Hao", "" ], [ "Jheng", "Ruei-Yu", "" ], [ "Wu", "Ji-Han", "" ], [ "Chiang", "Fan", "" ], [ "Chen", "Yen-Chi", "" ], [ "Wu", "Yuan-Yu", "" ], [ "Wu", "I-Chen", "" ] ]
2309.15577
Anthony Cohn
Anthony G Cohn
An Evaluation of ChatGPT-4's Qualitative Spatial Reasoning Capabilities in RCC-8
10 figures. 8 pages. Accepted for presentation at 36th International Workshop on Qualitative Reasoning (QR-23), in conjunction with ECAI2023 in Krakow, Poland
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Qualitative Spatial Reasoning (QSR) is well explored area of Commonsense Reasoning and has multiple applications ranging from Geographical Information Systems to Robotics and Computer Vision. Recently many claims have been made for the capabilities of Large Language Models (LLMs). In this paper we investigate the extent to which one particular LLM can perform classical qualitative spatial reasoning tasks on the mereotopological calculus, RCC-8.
[ { "version": "v1", "created": "Wed, 27 Sep 2023 11:23:15 GMT" } ]
1,695,859,200,000
[ [ "Cohn", "Anthony G", "" ] ]
2309.16146
Ming Wang
Ming Wang, Daling Wang, Wenfang Wu, Shi Feng, Yifei Zhang
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
To address the interpretability challenge in machine learning (ML) systems, counterfactual explanations (CEs) have emerged as a promising solution. CEs are unique as they provide workable suggestions to users, in addition to explaining why a certain outcome was predicted. The application of CEs encounters two main challenges: general user preferences and variable ML systems. User preferences tend to be general rather than specific, and CEs need to be adaptable to variable ML models while maintaining robustness even as these models change. Facing these challenges, we present a solution rooted in validated general user preferences, which are derived from thorough user research. We map these preferences to the properties of CEs. Additionally, we introduce a novel method, \uline{T}ree-based \uline{C}onditions \uline{O}ptional \uline{L}inks (T-COL), which incorporates two optional structures and multiple condition groups for generating CEs adaptable to general user preferences. Meanwhile, we employ T-COL to enhance the robustness of CEs with specific conditions, making them more valid even when the ML model is replaced. Our experimental comparisons under different user preferences show that T-COL outperforms all baselines, including Large Language Models which are shown to be able to generate counterfactuals.
[ { "version": "v1", "created": "Thu, 28 Sep 2023 03:51:49 GMT" }, { "version": "v2", "created": "Thu, 4 Apr 2024 05:59:22 GMT" } ]
1,712,275,200,000
[ [ "Wang", "Ming", "" ], [ "Wang", "Daling", "" ], [ "Wu", "Wenfang", "" ], [ "Feng", "Shi", "" ], [ "Zhang", "Yifei", "" ] ]
2309.16166
Stuart Armstrong
Stuart Armstrong and Alexandre Maranh\~ao and Oliver Daniels-Koch and Patrick Leask and Rebecca Gorman
CoinRun: Solving Goal Misgeneralisation
null
null
null
null
cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Goal misgeneralisation is a key challenge in AI alignment -- the task of getting powerful Artificial Intelligences to align their goals with human intentions and human morality. In this paper, we show how the ACE (Algorithm for Concept Extrapolation) agent can solve one of the key standard challenges in goal misgeneralisation: the CoinRun challenge. It uses no new reward information in the new environment. This points to how autonomous agents could be trusted to act in human interests, even in novel and critical situations.
[ { "version": "v1", "created": "Thu, 28 Sep 2023 04:43:39 GMT" }, { "version": "v2", "created": "Sat, 21 Oct 2023 20:08:46 GMT" }, { "version": "v3", "created": "Wed, 1 Nov 2023 17:23:46 GMT" } ]
1,698,883,200,000
[ [ "Armstrong", "Stuart", "" ], [ "Maranhão", "Alexandre", "" ], [ "Daniels-Koch", "Oliver", "" ], [ "Leask", "Patrick", "" ], [ "Gorman", "Rebecca", "" ] ]