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2403.03517

Tsz Ho Chan

Tsz Ho Chan, Wenyi Xiao, Junhua Huang, Huiling Zhen, Guangji Tian and Mingxuan Yuan

IB-Net: Initial Branch Network for Variable Decision in Boolean Satisfiability

7 pages, 12 figures

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Boolean Satisfiability problems are vital components in Electronic Design Automation, particularly within the Logic Equivalence Checking process. Currently, SAT solvers are employed for these problems and neural network is tried as assistance to solvers. However, as SAT problems in the LEC context are distinctive due to their predominantly unsatisfiability nature and a substantial proportion of UNSAT-core variables, existing neural network assistance has proven unsuccessful in this specialized domain. To tackle this challenge, we propose IB-Net, an innovative framework utilizing graph neural networks and novel graph encoding techniques to model unsatisfiable problems and interact with state-of-the-art solvers. Extensive evaluations across solvers and datasets demonstrate IB-Net's acceleration, achieving an average runtime speedup of 5.0% on industrial data and 8.3% on SAT competition data empirically. This breakthrough advances efficient solving in LEC workflows.

2403.03594

Yoshia Abe

Yoshia Abe, Tatsuya Daikoku, Yasuo Kuniyoshi

Assessing the Aesthetic Evaluation Capabilities of GPT-4 with Vision: Insights from Group and Individual Assessments

8 pages, 6 figures, submitted to The 38th Annual Conference of the Japanese Society for Artificial Intelligence, 2024

cs.AI

http://creativecommons.org/licenses/by/4.0/

Recently, it has been recognized that large language models demonstrate high performance on various intellectual tasks. However, few studies have investigated alignment with humans in behaviors that involve sensibility, such as aesthetic evaluation. This study investigates the performance of GPT-4 with Vision, a state-of-the-art language model that can handle image input, on the task of aesthetic evaluation of images. We employ two tasks, prediction of the average evaluation values of a group and an individual's evaluation values. We investigate the performance of GPT-4 with Vision by exploring prompts and analyzing prediction behaviors. Experimental results reveal GPT-4 with Vision's superior performance in predicting aesthetic evaluations and the nature of different responses to beauty and ugliness. Finally, we discuss developing an AI system for aesthetic evaluation based on scientific knowledge of the human perception of beauty, employing agent technologies that integrate traditional deep learning models with large language models.

2403.03600

Li Wang

Li Wang, Lei Sang, Quangui Zhang, Qiang Wu, Min Xu

A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation

cs.AI

http://creativecommons.org/licenses/by/4.0/

Cross-domain recommendation (CDR) aims to enhance recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the advantages of extracting domain-common and domain-specific features to learn comprehensive user and item representations. However, these methods can't effectively disentangle these components as they often rely on simple user-item historical interaction information (such as ratings, clicks, and browsing), neglecting the rich multi-modal features. Additionally, they don't protect user-sensitive data from potential leakage during knowledge transfer between domains. To address these challenges, we propose a Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation, called P2M2-CDR. Specifically, we first design a multi-modal disentangled encoder that utilizes multi-modal information to disentangle more informative domain-common and domain-specific embeddings. Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer. Local differential privacy (LDP) is utilized to obfuscate the disentangled embeddings before inter-domain exchange, thereby enhancing privacy protection. To ensure both consistency and differentiation among these obfuscated disentangled embeddings, we incorporate contrastive learning-based domain-inter and domain-intra losses. Extensive Experiments conducted on four real-world datasets demonstrate that P2M2-CDR outperforms other state-of-the-art single-domain and cross-domain baselines.

2403.03607

Johannes Hirth

Johannes Hirth, Tom Hanika

The Geometric Structure of Topic Models

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues.

2403.03645

Yucheng Wang

Yucheng Wang, Ruibing Jin, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen

K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data

12 pages,7 figures

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Sourced from various sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies, e.g., correlations among sensors. To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data. Typically, existing approaches construct graphs solely from MTS signals, which may introduce bias due to a small training dataset and may not accurately represent underlying dependencies. To address this challenge, we propose a novel framework named K-Link, leveraging Large Language Models (LLMs) to encode extensive general knowledge and thereby providing effective solutions to reduce the bias. Leveraging the knowledge embedded in LLMs, such as physical principles, we extract a \textit{Knowledge-Link graph}, capturing vast semantic knowledge of sensors and the linkage of the sensor-level knowledge. To harness the potential of the knowledge-link graph in enhancing the graph derived from MTS data, we propose a graph alignment module, facilitating the transfer of semantic knowledge within the knowledge-link graph into the MTS-derived graph. By doing so, we can improve the graph quality, ensuring effective representation learning with GNNs for MTS data. Extensive experiments demonstrate the efficacy of our approach for superior performance across various MTS-related downstream tasks.

2403.03744

Tessa Han

Tessa Han, Aounon Kumar, Chirag Agarwal, Himabindu Lakkaraju

Towards Safe Large Language Models for Medicine

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

As large language models (LLMs) develop ever-improving capabilities and are applied in real-world settings, it is important to understand their safety. While initial steps have been taken to evaluate the safety of general-knowledge LLMs, exposing some weaknesses, the safety of medical LLMs has not been sufficiently evaluated despite their high risks to personal health and safety, public health and safety, patient rights, and human rights. To address this gap, we conduct, to our knowledge, the first study of its kind to evaluate and improve the safety of medical LLMs. We find that 1) current medical LLMs do not meet standards of general or medical safety, as they readily comply with harmful requests and that 2) fine-tuning medical LLMs on safety demonstrations significantly improves their safety, reducing their tendency to comply with harmful requests. In addition, we present a definition of medical safety for LLMs and develop a benchmark dataset to evaluate and train for medical safety in LLMs. Poised at the intersection of research on machine learning safety and medical machine learning, this work casts light on the status quo of the safety of medical LLMs and motivates future work in this area, mitigating the risks of harm of LLMs in medicine.

2403.03828

Rushit Dave

Rushit Dave, Marcho Handoko, Ali Rashid, Cole Schoenbauer

From Clicks to Security: Investigating Continuous Authentication via Mouse Dynamics

cs.AI

http://creativecommons.org/licenses/by/4.0/

In the realm of computer security, the importance of efficient and reliable user authentication methods has become increasingly critical. This paper examines the potential of mouse movement dynamics as a consistent metric for continuous authentication. By analyzing user mouse movement patterns in two contrasting gaming scenarios, "Team Fortress" and Poly Bridge we investigate the distinctive behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond conventional methodologies by employing a range of machine learning models. These models are carefully selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as reflected in their mouse movements. This multifaceted approach allows for a more nuanced and comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine learning models employed in this study demonstrate competent performance in user verification, marking an improvement over previous methods used in this field. This research contributes to the ongoing efforts to enhance computer security and highlights the potential of leveraging user behavior, specifically mouse dynamics, in developing robust authentication systems.

2403.03832

Rushit Dave

Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt, Rushit Dave

Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning

cs.AI

http://creativecommons.org/licenses/by/4.0/

This research aims to further understanding in the field of continuous authentication using behavioral biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch dynamics can effectively distinguish users. However, further studies are needed to make it viable option for authentication systems

2403.03996

Kai Yin

Zhewei Liu, Kai Yin, Ali Mostafavi

Rethinking Urban Flood Risk Assessment By Adapting Health Domain Perspective

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Inspired by ideas from health risk assessment, this paper presents a new perspective for flood risk assessment. The proposed perspective focuses on three pillars for examining flood risk: (1) inherent susceptibility, (2) mitigation strategies, and (3) external stressors. These pillars collectively encompass the physical and environmental characteristics of urban areas, the effectiveness of human-intervention measures, and the influence of uncontrollable external factors, offering a fresh point of view for decoding flood risks. For each pillar, we delineate its individual contributions to flood risk and illustrate their interactive and overall impact. The three-pillars model embodies a shift in focus from the quest to precisely model and quantify flood risk to evaluating pathways to high flood risk. The shift in perspective is intended to alleviate the quest for quantifying and predicting flood risk at fine resolutions as a panacea for enhanced flood risk management. The decomposition of flood risk pathways into the three intertwined pillars (i.e., inherent factors, mitigation factors, and external factors) enables evaluation of changes in factors within each pillar enhance and exacerbate flood risk, creating a platform from which to inform plans, decisions, and actions. Building on this foundation, we argue that a flood risk pathway analysis approach, which examines the individual and collective impacts of inherent factors, mitigation strategies, and external stressors, is essential for a nuanced evaluation of flood risk. Accordingly, the proposed perspective could complement the existing frameworks and approaches for flood risk assessment.

2403.03997

Yixuan Li

Yixuan Li, Julian Parsert, Elizabeth Polgreen

Guiding Enumerative Program Synthesis with Large Language Models

Accepted at CAV 2024

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Pre-trained Large Language Models (LLMs) are beginning to dominate the discourse around automatic code generation with natural language specifications. In contrast, the best-performing synthesizers in the domain of formal synthesis with precise logical specifications are still based on enumerative algorithms. In this paper, we evaluate the abilities of LLMs to solve formal synthesis benchmarks by carefully crafting a library of prompts for the domain. When one-shot synthesis fails, we propose a novel enumerative synthesis algorithm, which integrates calls to an LLM into a weighted probabilistic search. This allows the synthesizer to provide the LLM with information about the progress of the enumerator, and the LLM to provide the enumerator with syntactic guidance in an iterative loop. We evaluate our techniques on benchmarks from the Syntax-Guided Synthesis (SyGuS) competition. We find that GPT-3.5 as a stand-alone tool for formal synthesis is easily outperformed by state-of-the-art formal synthesis algorithms, but our approach integrating the LLM into an enumerative synthesis algorithm shows significant performance gains over both the LLM and the enumerative synthesizer alone and the winning SyGuS competition tool.

2403.04087

Nik Bear Brown

Nik Bear Brown

The Cognitive Type Project -- Mapping Typography to Cognition

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The Cognitive Type Project is focused on developing computational tools to enable the design of typefaces with varying cognitive properties. This initiative aims to empower typographers to craft fonts that enhance click-through rates for online ads, improve reading levels in children's books, enable dyslexics to create personalized type, or provide insights into customer reactions to textual content in media. A significant challenge in research related to mapping typography to cognition is the creation of thousands of typefaces with minor variations, a process that is both labor-intensive and requires the expertise of skilled typographers. Cognitive science research highlights that the design and form of letters, along with the text's overall layout, are crucial in determining the ease of reading and other cognitive properties of type such as perceived beauty and memorability. These factors affect not only the legibility and clarity of information presentation but also the likability of a typeface.

2403.04105

Lekang Jiang

Lekang Jiang, Stephan Goetz

Artificial Intelligence Exploring the Patent Field

53 pages, 14 figures, 5 tables

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Advanced language-processing and machine-learning techniques promise massive efficiency improvements in the previously widely manual field of patent and technical knowledge management. This field presents large-scale and complex data with very precise contents and language representation of those contents. Particularly, patent texts can differ from mundane texts in various aspects, which entails significant opportunities and challenges. This paper presents a systematic overview of patent-related tasks and popular methodologies with a special focus on evolving and promising techniques. Language processing and particularly large language models as well as the recent boost of general generative methods promise to become game changers in the patent field. The patent literature and the fact-based argumentative procedures around patents appear almost as an ideal use case. However, patents entail a number of difficulties with which existing models struggle. The paper introduces fundamental aspects of patents and patent-related data that affect technology that wants to explore or manage them. It further reviews existing methods and approaches and points out how important reliable and unbiased evaluation metrics become. Although research has made substantial progress on certain tasks, the performance across many others remains suboptimal, sometimes because of either the special nature of patents and their language or inconsistencies between legal terms and the everyday meaning of terms. Moreover, yet few methods have demonstrated the ability to produce satisfactory text for specific sections of patents. By pointing out key developments, opportunities, and gaps, we aim to encourage further research and accelerate the advancement of this field.

2403.04106

Matthew Greenig

Elsa Lawrence, Adham El-Shazly, Srijit Seal, Chaitanya K Joshi, Pietro Li\`o, Shantanu Singh, Andreas Bender, Pietro Sormanni, Matthew Greenig

Understanding Biology in the Age of Artificial Intelligence

cs.AI

http://creativecommons.org/licenses/by/4.0/

Modern life sciences research is increasingly relying on artificial intelligence approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research - protein structure prediction and single cell RNA-sequencing - we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems.

2403.04124

Longchao Da

Tiejin Chen, Longchao Da, Huixue Zhou, Pingzhi Li, Kaixiong Zhou, Tianlong Chen, Hua Wei

Privacy-preserving Fine-tuning of Large Language Models through Flatness

Accepted to ICLR 2024 SeT LLM Workshop

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT. Differential Privacy (DP) techniques are explored in existing work to mitigate their privacy risks at the cost of generalization degradation. Our paper reveals that the flatness of DP-trained models' loss landscape plays an essential role in the trade-off between their privacy and generalization. We further propose a holistic framework to enforce appropriate weight flatness, which substantially improves model generalization with competitive privacy preservation. It innovates from three coarse-to-grained levels, including perturbation-aware min-max optimization on model weights within a layer, flatness-guided sparse prefix-tuning on weights across layers, and weight knowledge distillation between DP \& non-DP weights copies. Comprehensive experiments of both black-box and white-box scenarios are conducted to demonstrate the effectiveness of our proposal in enhancing generalization and maintaining DP characteristics. For instance, on text classification dataset QNLI, DP-Flat achieves similar performance with non-private full fine-tuning but with DP guarantee under privacy budget $\epsilon=3$, and even better performance given higher privacy budgets. Codes are provided in the supplement.

2403.04135

Yui Uehara

Yui Uehara

Unsupervised Learning of Harmonic Analysis Based on Neural HSMM with Code Quality Templates

20 pages, 5 figures, the original edition of this paper will be published in the ICNMC2024 Proceedings and this arXiv publication is a copy

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper presents a method of unsupervised learning of harmonic analysis based on a hidden semi-Markov model (HSMM). We introduce the chord quality templates, which specify the probability of pitch class emissions given a root note and a chord quality. Other probability distributions that comprise the HSMM are automatically learned via unsupervised learning, which has been a challenge in existing research. The results of the harmonic analysis of the proposed model were evaluated using existing labeled data. While our proposed method has yet to perform as well as existing models that used supervised learning and complex rule design, it has the advantage of not requiring expensive labeled data or rule elaboration. Furthermore, we also show how to recognize the tonic without prior knowledge, based on the transition probabilities of the Markov model.

2403.04140

Biqing Qi

Biqing Qi, Junqi Gao, Xingquan Chen, Dong Li, Jianxing Liu, Ligang Wu and Bowen Zhou

Contrastive Augmented Graph2Graph Memory Interaction for Few Shot Continual Learning

12 Pages, 5 figures

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention in recent years for its pivotal role in addressing continuously arriving classes. However, it encounters additional challenges. The scarcity of samples in new sessions intensifies overfitting, causing incompatibility between the output features of new and old classes, thereby escalating catastrophic forgetting. A prevalent strategy involves mitigating catastrophic forgetting through the Explicit Memory (EM), which comprise of class prototypes. However, current EM-based methods retrieves memory globally by performing Vector-to-Vector (V2V) interaction between features corresponding to the input and prototypes stored in EM, neglecting the geometric structure of local features. This hinders the accurate modeling of their positional relationships. To incorporate information of local geometric structure, we extend the V2V interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures for better G2G alignment and the prevention of local feature collapse, we propose the Local Graph Preservation (LGP) mechanism. Additionally, to address sample scarcity in classes from new sessions, the Contrast-Augmented G2G (CAG2G) is introduced to promote the aggregation of same class features thus helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the challenging ImageNet-R dataset demonstrate the superiority of our method over existing methods.

2403.04264

Hoang Giang Pham

Hoang Giang Pham, Tien Thanh Dam, Ngan Ha Duong, Tien Mai and Minh Hoang Ha

Competitive Facility Location under Random Utilities and Routing Constraints

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this paper, we study a facility location problem within a competitive market context, where customer demand is predicted by a random utility choice model. Unlike prior research, which primarily focuses on simple constraints such as a cardinality constraint on the number of selected locations, we introduce routing constraints that necessitate the selection of locations in a manner that guarantees the existence of a tour visiting all chosen locations while adhering to a specified tour length upper bound. Such routing constraints find crucial applications in various real-world scenarios. The problem at hand features a non-linear objective function, resulting from the utilization of random utilities, together with complex routing constraints, making it computationally challenging. To tackle this problem, we explore three types of valid cuts, namely, outer-approximation and submodular cuts to handle the nonlinear objective function, as well as sub-tour elimination cuts to address the complex routing constraints. These lead to the development of two exact solution methods: a nested cutting plane and nested branch-and-cut algorithms, where these valid cuts are iteratively added to a master problem through two nested loops. We also prove that our nested cutting plane method always converges to optimality after a finite number of iterations. Furthermore, we develop a local search-based metaheuristic tailored for solving large-scale instances and show its pros and cons compared to exact methods. Extensive experiments are conducted on problem instances of varying sizes, demonstrating that our approach excels in terms of solution quality and computation time when compared to other baseline approaches.

2403.04292

Knud Thomsen

Knud Thomsen

A challenge in A(G)I, cybernetics revived in the Ouroboros Model as one algorithm for all thinking

26 pages, 11 figures

Artificial Intelligence and Autonomous Systems Volume 1 Issue 1, 2024

10.55092/aias20240001

cs.AI

http://creativecommons.org/licenses/by/4.0/

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented in the form of a drawing for AI to understand. In a second vein, AI is challenged to produce something similar from verbal description. The aim of the paper is to highlight strengths and deficiencies of current Artificial Intelligence approaches while coarsely sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities. Reality, rules, truth, and Free Will are all useful abstractions according to the Ouroboros Model. Logic deduction as well as intuitive guesses are claimed as produced on the basis of one compartmentalized memory for schemata and a pattern-matching, i.e., monitoring process termed consumption analysis. The latter directs attention on short (attention proper) and also on long times scales (emotional biases). In this cybernetic approach, discrepancies between expectations and actual activations (e.g., sensory precepts) drive the general process of cognition and at the same time steer the storage of new and adapted memory entries. Dedicated structures in the human brain work in concert according to this scheme.

2403.04343

Yanqi Dai

Yanqi Dai, Dong Jing, Nanyi Fei, Zhiwu Lu

CoTBal: Comprehensive Task Balancing for Multi-Task Visual Instruction Tuning

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Visual instruction tuning is a key training stage of large multimodal models (LMMs). Nevertheless, the common practice of indiscriminately mixing instruction-following data from various tasks may result in suboptimal overall performance due to different instruction formats and knowledge domains across tasks. To mitigate this issue, we propose a novel Comprehensive Task Balancing (CoTBal) algorithm for multi-task visual instruction tuning of LMMs. To our knowledge, this is the first work that explores multi-task optimization in visual instruction tuning. Specifically, we consider two key dimensions for task balancing: (1) Inter-Task Contribution, the phenomenon where learning one task potentially enhances the performance in other tasks, attributable to the overlapping knowledge domains, and (2) Intra-Task Difficulty, which refers to the learning difficulty within a single task. By quantifying these two dimensions with performance-based metrics, task balancing is thus enabled by assigning more weights to tasks that offer substantial contributions to others, receive minimal contributions from others, and also have great intra-task difficulties. Experiments show that our CoTBal leads to superior overall performance in multi-task visual instruction tuning.

2403.04366

Ang Li

Ang Li, Yiquan Wu, Yifei Liu, Fei Wu, Ming Cai, Kun Kuang

Enhancing Court View Generation with Knowledge Injection and Guidance

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Court View Generation (CVG) is a challenging task in the field of Legal Artificial Intelligence (LegalAI), which aims to generate court views based on the plaintiff claims and the fact descriptions. While Pretrained Language Models (PLMs) have showcased their prowess in natural language generation, their application to the complex, knowledge-intensive domain of CVG often reveals inherent limitations. In this paper, we present a novel approach, named Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To efficiently incorporate domain knowledge during the training stage, we introduce a knowledge-injected prompt encoder for prompt tuning, thereby reducing computational overhead. Moreover, to further enhance the model's ability to utilize domain knowledge, we employ a generating navigator, which dynamically guides the text generation process in the inference stage without altering the model's architecture, making it readily transferable. Comprehensive experiments on real-world data demonstrate the effectiveness of our approach compared to several established baselines, especially in the responsivity of claims, where it outperforms the best baseline by 11.87%.

2403.04449

Natalie Kiesler

Imen Azaiz, Natalie Kiesler, Sven Strickroth

Feedback-Generation for Programming Exercises With GPT-4

accepted at ITiCSE 2024, Milan, Italy

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Ever since Large Language Models (LLMs) and related applications have become broadly available, several studies investigated their potential for assisting educators and supporting students in higher education. LLMs such as Codex, GPT-3.5, and GPT 4 have shown promising results in the context of large programming courses, where students can benefit from feedback and hints if provided timely and at scale. This paper explores the quality of GPT-4 Turbo's generated output for prompts containing both the programming task specification and a student's submission as input. Two assignments from an introductory programming course were selected, and GPT-4 was asked to generate feedback for 55 randomly chosen, authentic student programming submissions. The output was qualitatively analyzed regarding correctness, personalization, fault localization, and other features identified in the material. Compared to prior work and analyses of GPT-3.5, GPT-4 Turbo shows notable improvements. For example, the output is more structured and consistent. GPT-4 Turbo can also accurately identify invalid casing in student programs' output. In some cases, the feedback also includes the output of the student program. At the same time, inconsistent feedback was noted such as stating that the submission is correct but an error needs to be fixed. The present work increases our understanding of LLMs' potential, limitations, and how to integrate them into e-assessment systems, pedagogical scenarios, and instructing students who are using applications based on GPT-4.

2403.04471

Elliott Thornley

Elliott Thornley

The Shutdown Problem: An AI Engineering Puzzle for Decision Theorists

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

I explain the shutdown problem: the problem of designing artificial agents that (1) shut down when a shutdown button is pressed, (2) don't try to prevent or cause the pressing of the shutdown button, and (3) otherwise pursue goals competently. I prove three theorems that make the difficulty precise. These theorems show that agents satisfying some innocuous-seeming conditions will often try to prevent or cause the pressing of the shutdown button, even in cases where it's costly to do so. And patience trades off against shutdownability: the more patient an agent, the greater the costs that agent is willing to incur to manipulate the shutdown button. I end by noting that these theorems can guide our search for solutions.

2403.04504

Jaehyun Lee

Jaehyun Lee, SeongKu Kang, Hwanjo Yu

Improving Matrix Completion by Exploiting Rating Ordinality in Graph Neural Networks

4 pages, 2 figures, 3 tables

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Matrix completion is an important area of research in recommender systems. Recent methods view a rating matrix as a user-item bi-partite graph with labeled edges denoting observed ratings and predict the edges between the user and item nodes by using the graph neural network (GNN). Despite their effectiveness, they treat each rating type as an independent relation type and thus cannot sufficiently consider the ordinal nature of the ratings. In this paper, we explore a new approach to exploit rating ordinality for GNN, which has not been studied well in the literature. We introduce a new method, called ROGMC, to leverage Rating Ordinality in GNN-based Matrix Completion. It uses cumulative preference propagation to directly incorporate rating ordinality in GNN's message passing, allowing for users' stronger preferences to be more emphasized based on inherent orders of rating types. This process is complemented by interest regularization which facilitates preference learning using the underlying interest information. Our extensive experiments show that ROGMC consistently outperforms the existing strategies of using rating types for GNN. We expect that our attempt to explore the feasibility of utilizing rating ordinality for GNN may stimulate further research in this direction.

2403.04511

Nicholas Sukiennik

Nicholas Sukiennik, Chen Gao, Nian Li

Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video Recommendation

accepted to WWW 2024

10.1145/3589334.3648159

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Filter bubbles have been studied extensively within the context of online content platforms due to their potential to cause undesirable outcomes such as user dissatisfaction or polarization. With the rise of short-video platforms, the filter bubble has been given extra attention because these platforms rely on an unprecedented use of the recommender system to provide relevant content. In our work, we investigate the deep filter bubble, which refers to the user being exposed to narrow content within their broad interests. We accomplish this using one-year interaction data from a top short-video platform in China, which includes hierarchical data with three levels of categories for each video. We formalize our definition of a "deep" filter bubble within this context, and then explore various correlations within the data: first understanding the evolution of the deep filter bubble over time, and later revealing some of the factors that give rise to this phenomenon, such as specific categories, user demographics, and feedback type. We observe that while the overall proportion of users in a filter bubble remains largely constant over time, the depth composition of their filter bubble changes. In addition, we find that some demographic groups that have a higher likelihood of seeing narrower content and implicit feedback signals can lead to less bubble formation. Finally, we propose some ways in which recommender systems can be designed to reduce the risk of a user getting caught in a bubble.

2403.04541

Irfan Kareem

Manuel Borroto, Irfan Kareem, Francesco Ricca

Towards Automatic Composition of ASP Programs from Natural Language Specifications

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper moves the first step towards automating the composition of Answer Set Programming (ASP) specifications. In particular, the following contributions are provided: (i) A dataset focused on graph-related problem specifications, designed to develop and assess tools for ASP automatic coding; (ii) A two-step architecture, implemented in the NL2ASP tool, for generating ASP programs from natural language specifications. NL2ASP uses neural machine translation to transform natural language into Controlled Natural Language (CNL) statements. Subsequently, CNL statements are converted into ASP code using the CNL2ASP tool. An experiment confirms the viability of the approach.

2403.04571

Nikolay Malkin

Yoshua Bengio, Nikolay Malkin

Machine learning and information theory concepts towards an AI Mathematician

To appear in the Bulletin of the AMS, 2024

cs.AI

http://creativecommons.org/licenses/by/4.0/

The current state-of-the-art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities -- which correspond to our intuition and habitual behaviors -- but still lacks something important regarding system 2 abilities -- which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.

2403.04588

L\'eopold Mayti\'e

L\'eopold Mayti\'e, Benjamin Devillers, Alexandre Arnold, Rufin VanRullen

Zero-shot cross-modal transfer of Reinforcement Learning policies through a Global Workspace

Under review in a conference

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Humans perceive the world through multiple senses, enabling them to create a comprehensive representation of their surroundings and to generalize information across domains. For instance, when a textual description of a scene is given, humans can mentally visualize it. In fields like robotics and Reinforcement Learning (RL), agents can also access information about the environment through multiple sensors; yet redundancy and complementarity between sensors is difficult to exploit as a source of robustness (e.g. against sensor failure) or generalization (e.g. transfer across domains). Prior research demonstrated that a robust and flexible multimodal representation can be efficiently constructed based on the cognitive science notion of a 'Global Workspace': a unique representation trained to combine information across modalities, and to broadcast its signal back to each modality. Here, we explore whether such a brain-inspired multimodal representation could be advantageous for RL agents. First, we train a 'Global Workspace' to exploit information collected about the environment via two input modalities (a visual input, or an attribute vector representing the state of the agent and/or its environment). Then, we train a RL agent policy using this frozen Global Workspace. In two distinct environments and tasks, our results reveal the model's ability to perform zero-shot cross-modal transfer between input modalities, i.e. to apply to image inputs a policy previously trained on attribute vectors (and vice-versa), without additional training or fine-tuning. Variants and ablations of the full Global Workspace (including a CLIP-like multimodal representation trained via contrastive learning) did not display the same generalization abilities.

2403.04859

Akansh Maurya

Akansh Maurya, Hewan Shrestha, Mohammad Munem Shahriar

Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images

cs.AI

http://creativecommons.org/licenses/by/4.0/

With the limited availability of labeled data with various atmospheric conditions in remote sensing images, it seems useful to work with self-supervised algorithms. Few pretext-based algorithms, including from rotation, spatial context and jigsaw puzzles are not appropriate for satellite images. Often, satellite images have a higher temporal frequency. So, the temporal dimension of remote sensing data provides natural augmentation without requiring us to create artificial augmentation of images. Here, we propose S3-TSS, a novel method of self-supervised learning technique that leverages natural augmentation occurring in temporal dimension. We compare our results with current state-of-the-art methods and also perform various experiments. We observed that our method was able to perform better than baseline SeCo in four downstream datasets. Code for our work can be found here: https://github.com/hewanshrestha/Why-Self-Supervision-in-Time

2403.04866

Marco D'Alessandro

Marco D Alessandro, Enrique Calabr\'es, Mikel Elkano

A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data

8 pages, 1 figure

cs.AI

http://creativecommons.org/licenses/by/4.0/

Multimodal learning is a rapidly growing research field that has revolutionized multitasking and generative modeling in AI. While much of the research has focused on dealing with unstructured data (e.g., language, images, audio, or video), structured data (e.g., tabular data, time series, or signals) has received less attention. However, many industry-relevant use cases involve or can be benefited from both types of data. In this work, we propose a modular, end-to-end multimodal learning method called MAGNUM, which can natively handle both structured and unstructured data. MAGNUM is flexible enough to employ any specialized unimodal module to extract, compress, and fuse information from all available modalities.

2403.04893

Shayne Longpre

Shayne Longpre, Sayash Kapoor, Kevin Klyman, Ashwin Ramaswami, Rishi Bommasani, Borhane Blili-Hamelin, Yangsibo Huang, Aviya Skowron, Zheng-Xin Yong, Suhas Kotha, Yi Zeng, Weiyan Shi, Xianjun Yang, Reid Southen, Alexander Robey, Patrick Chao, Diyi Yang, Ruoxi Jia, Daniel Kang, Sandy Pentland, Arvind Narayanan, Percy Liang, Peter Henderson

A Safe Harbor for AI Evaluation and Red Teaming

cs.AI

http://creativecommons.org/licenses/by/4.0/

Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems. However, the terms of service and enforcement strategies used by prominent AI companies to deter model misuse have disincentives on good faith safety evaluations. This causes some researchers to fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. Although some companies offer researcher access programs, they are an inadequate substitute for independent research access, as they have limited community representation, receive inadequate funding, and lack independence from corporate incentives. We propose that major AI developers commit to providing a legal and technical safe harbor, indemnifying public interest safety research and protecting it from the threat of account suspensions or legal reprisal. These proposals emerged from our collective experience conducting safety, privacy, and trustworthiness research on generative AI systems, where norms and incentives could be better aligned with public interests, without exacerbating model misuse. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.

2403.04957

Xiaogeng Liu

Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang, Ning Zhang, Chaowei Xiao

Automatic and Universal Prompt Injection Attacks against Large Language Models

Pre-print, code is available at https://github.com/SheltonLiu-N/Universal-Prompt-Injection

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. However, their capabilities can be exploited through prompt injection attacks. These attacks manipulate LLM-integrated applications into producing responses aligned with the attacker's injected content, deviating from the user's actual requests. The substantial risks posed by these attacks underscore the need for a thorough understanding of the threats. Yet, research in this area faces challenges due to the lack of a unified goal for such attacks and their reliance on manually crafted prompts, complicating comprehensive assessments of prompt injection robustness. We introduce a unified framework for understanding the objectives of prompt injection attacks and present an automated gradient-based method for generating highly effective and universal prompt injection data, even in the face of defensive measures. With only five training samples (0.3% relative to the test data), our attack can achieve superior performance compared with baselines. Our findings emphasize the importance of gradient-based testing, which can avoid overestimation of robustness, especially for defense mechanisms.

2403.05000

Pengcheng Li

Jianzong Wang, Pengcheng Li, Xulong Zhang, Ning Cheng, Jing Xiao

Medical Speech Symptoms Classification via Disentangled Representation

Accepted by the 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2024)

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Intent is defined for understanding spoken language in existing works. Both textual features and acoustic features involved in medical speech contain intent, which is important for symptomatic diagnosis. In this paper, we propose a medical speech classification model named DRSC that automatically learns to disentangle intent and content representations from textual-acoustic data for classification. The intent representations of the text domain and the Mel-spectrogram domain are extracted via intent encoders, and then the reconstructed text feature and the Mel-spectrogram feature are obtained through two exchanges. After combining the intent from two domains into a joint representation, the integrated intent representation is fed into a decision layer for classification. Experimental results show that our model obtains an average accuracy rate of 95% in detecting 25 different medical symptoms.

2403.05025

Dingkang Yang

Dingkang Yang, Dongling Xiao, Ke Li, Yuzheng Wang, Zhaoyu Chen, Jinjie Wei, Lihua Zhang

Towards Multimodal Human Intention Understanding Debiasing via Subject-Deconfounding

14 pages

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Multimodal intention understanding (MIU) is an indispensable component of human expression analysis (e.g., sentiment or humor) from heterogeneous modalities, including visual postures, linguistic contents, and acoustic behaviors. Existing works invariably focus on designing sophisticated structures or fusion strategies to achieve impressive improvements. Unfortunately, they all suffer from the subject variation problem due to data distribution discrepancies among subjects. Concretely, MIU models are easily misled by distinct subjects with different expression customs and characteristics in the training data to learn subject-specific spurious correlations, significantly limiting performance and generalizability across uninitiated subjects.Motivated by this observation, we introduce a recapitulative causal graph to formulate the MIU procedure and analyze the confounding effect of subjects. Then, we propose SuCI, a simple yet effective causal intervention module to disentangle the impact of subjects acting as unobserved confounders and achieve model training via true causal effects. As a plug-and-play component, SuCI can be widely applied to most methods that seek unbiased predictions. Comprehensive experiments on several MIU benchmarks clearly demonstrate the effectiveness of the proposed module.

2403.05029

Chengyang Zhang

Chengyang Zhang, Yong Zhang, Qitan Shao, Jiangtao Feng, Bo Li, Yisheng Lv, Xinglin Piao, Baocai Yin

BjTT: A Large-scale Multimodal Dataset for Traffic Prediction

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Traffic prediction is one of the most significant foundations in Intelligent Transportation Systems (ITS). Traditional traffic prediction methods rely only on historical traffic data to predict traffic trends and face two main challenges. 1) insensitivity to unusual events. 2) limited performance in long-term prediction. In this work, we explore how generative models combined with text describing the traffic system can be applied for traffic generation, and name the task Text-to-Traffic Generation (TTG). The key challenge of the TTG task is how to associate text with the spatial structure of the road network and traffic data for generating traffic situations. To this end, we propose ChatTraffic, the first diffusion model for text-to-traffic generation. To guarantee the consistency between synthetic and real data, we augment a diffusion model with the Graph Convolutional Network (GCN) to extract spatial correlations of traffic data. In addition, we construct a large dataset containing text-traffic pairs for the TTG task. We benchmarked our model qualitatively and quantitatively on the released dataset. The experimental results indicate that ChatTraffic can generate realistic traffic situations from the text. Our code and dataset are available at https://github.com/ChyaZhang/ChatTraffic.

2403.05112

Tanvi Verma

Tanvi Verma, Linh Le Dinh, Nicholas Tan, Xinxing Xu, Chingyu Cheng, Yong Liu

RLPeri: Accelerating Visual Perimetry Test with Reinforcement Learning and Convolutional Feature Extraction

Published at AAAI-24

The 38th Annual AAAI Conference on Artificial Intelligence, 2024

cs.AI

http://creativecommons.org/licenses/by/4.0/

Visual perimetry is an important eye examination that helps detect vision problems caused by ocular or neurological conditions. During the test, a patient's gaze is fixed at a specific location while light stimuli of varying intensities are presented in central and peripheral vision. Based on the patient's responses to the stimuli, the visual field mapping and sensitivity are determined. However, maintaining high levels of concentration throughout the test can be challenging for patients, leading to increased examination times and decreased accuracy. In this work, we present RLPeri, a reinforcement learning-based approach to optimize visual perimetry testing. By determining the optimal sequence of locations and initial stimulus values, we aim to reduce the examination time without compromising accuracy. Additionally, we incorporate reward shaping techniques to further improve the testing performance. To monitor the patient's responses over time during testing, we represent the test's state as a pair of 3D matrices. We apply two different convolutional kernels to extract spatial features across locations as well as features across different stimulus values for each location. Through experiments, we demonstrate that our approach results in a 10-20% reduction in examination time while maintaining the accuracy as compared to state-of-the-art methods. With the presented approach, we aim to make visual perimetry testing more efficient and patient-friendly, while still providing accurate results.

2403.05130

Wangtao Sun

Wangtao Sun, Shizhu He, Jun Zhao, Kang Liu

From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphs

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

With good explanatory power and controllability, rule-based methods play an important role in many tasks such as knowledge reasoning and decision support. However, existing studies primarily focused on learning chain-like rules, which limit their semantic expressions and accurate prediction abilities. As a result, chain-like rules usually fire on the incorrect grounding values, producing inaccurate or even erroneous reasoning results. In this paper, we propose the concept of tree-like rules on knowledge graphs to expand the application scope and improve the reasoning ability of rule-based methods. Meanwhile, we propose an effective framework for refining chain-like rules into tree-like rules. Experimental comparisons on four public datasets show that the proposed framework can easily adapt to other chain-like rule induction methods and the refined tree-like rules consistently achieve better performances than chain-like rules on link prediction. The data and code of this paper can be available at https://anonymous.4open.science/r/tree-rule-E3CD/.

2403.05229

Nan Liu

Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data

cs.AI

http://creativecommons.org/licenses/by/4.0/

Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume that data originates from a single source, posing privacy challenges in collaborations with multiple data owners. We propose a novel framework for building federated scoring systems for multi-site survival outcomes, ensuring both privacy and communication efficiency. We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States. Additionally, we independently developed local scores at each site. In testing datasets from each participant site, our proposed federated scoring system consistently outperformed all local models, evidenced by higher integrated area under the receiver operating characteristic curve (iAUC) values, with a maximum improvement of 11.6%. Additionally, the federated score's time-dependent AUC(t) values showed advantages over local scores, exhibiting narrower confidence intervals (CIs) across most time points. The model developed through our proposed method exhibits effective performance on each local site, signifying noteworthy implications for healthcare research. Sites participating in our proposed federated scoring model training gained benefits by acquiring survival models with enhanced prediction accuracy and efficiency. This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data.

2403.05260

Hui Liu

Wei Duan, Hui Liu

Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for Adversarial Multi-source Domain Adaptation

cs.AI

http://creativecommons.org/licenses/by/4.0/

The development of single-cell sequencing technology had promoted the generation of a large amount of single-cell transcriptional profiles, providing valuable opportunities to explore drug-resistant cell subpopulations in a tumor. However, the drug sensitivity data in single-cell level is still scarce to date, pressing an urgent and highly challenging task for computational prediction of the drug sensitivity to individual cells. This paper proposed scAdaDrug, a multi-source adaptive weighting model to predict single-cell drug sensitivity. We used an autoencoder to extract domain-invariant features related to drug sensitivity from multiple source domains by exploiting adversarial domain adaptation. Especially, we introduced an adaptive weight generator to produce importance-aware and mutual independent weights, which could adaptively modulate the embedding of each sample in dimension-level for both source and target domains. Extensive experimental results showed that our model achieved state-of-the-art performance in predicting drug sensitivity on sinle-cell datasets, as well as on cell line and patient datasets.

2403.05265

Zinan Zeng

Zinan Zeng, Sen Ye, Zijian Cai, Heng Wang, Yuhan Liu, Haokai Zhang, Minnan Luo

MMoE: Robust Spoiler Detection with Multi-modal Information and Domain-aware Mixture-of-Experts

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Online movie review websites are valuable for information and discussion about movies. However, the massive spoiler reviews detract from the movie-watching experience, making spoiler detection an important task. Previous methods simply focus on reviews' text content, ignoring the heterogeneity of information in the platform. For instance, the metadata and the corresponding user's information of a review could be helpful. Besides, the spoiler language of movie reviews tends to be genre-specific, thus posing a domain generalization challenge for existing methods. To this end, we propose MMoE, a multi-modal network that utilizes information from multiple modalities to facilitate robust spoiler detection and adopts Mixture-of-Experts to enhance domain generalization. MMoE first extracts graph, text, and meta feature from the user-movie network, the review's textual content, and the review's metadata respectively. To handle genre-specific spoilers, we then adopt Mixture-of-Experts architecture to process information in three modalities to promote robustness. Finally, we use an expert fusion layer to integrate the features from different perspectives and make predictions based on the fused embedding. Experiments demonstrate that MMoE achieves state-of-the-art performance on two widely-used spoiler detection datasets, surpassing previous SOTA methods by 2.56% and 8.41% in terms of accuracy and F1-score. Further experiments also demonstrate MMoE's superiority in robustness and generalization.

2403.05307

Jinyang Li

Jinyang Li, Nan Huo, Yan Gao, Jiayi Shi, Yingxiu Zhao, Ge Qu, Yurong Wu, Chenhao Ma, Jian-Guang Lou, Reynold Cheng

Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents

30 pages, 7 figures

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Interactive Data Analysis, the collaboration between humans and LLM agents, enables real-time data exploration for informed decision-making. The challenges and costs of collecting realistic interactive logs for data analysis hinder the quantitative evaluation of Large Language Model (LLM) agents in this task. To mitigate this issue, we introduce Tapilot-Crossing, a new benchmark to evaluate LLM agents on interactive data analysis. Tapilot-Crossing contains 1024 interactions, covering 4 practical scenarios: Normal, Action, Private, and Private Action. Notably, Tapilot-Crossing is constructed by an economical multi-agent environment, Decision Company, with few human efforts. We evaluate popular and advanced LLM agents in Tapilot-Crossing, which underscores the challenges of interactive data analysis. Furthermore, we propose Adaptive Interaction Reflection (AIR), a self-generated reflection strategy that guides LLM agents to learn from successful history. Experiments demonstrate that Air can evolve LLMs into effective interactive data analysis agents, achieving a relative performance improvement of up to 44.5%.

2403.05407

Abdolmahdi Bagheri

Abdolmahdi Bagheri, Mahdi Dehshiri, Babak Nadjar Araabi, Alireza Akhondi Asl

Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networks

cs.AI

http://creativecommons.org/licenses/by/4.0/

In the investigation of any causal mechanisms, such as the brain's causal networks, the assumption of causal sufficiency plays a critical role. Notably, neglecting this assumption can result in significant errors, a fact that is often disregarded in the causal analysis of brain networks. In this study, we propose an algorithmic identification approach for determining essential exogenous nodes that satisfy the critical need for causal sufficiency to adhere to it in such inquiries. Our approach consists of three main steps: First, by capturing the essence of the Peter-Clark (PC) algorithm, we conduct independence tests for pairs of regions within a network, as well as for the same pairs conditioned on nodes from other networks. Next, we distinguish candidate confounders by analyzing the differences between the conditional and unconditional results, using the Kolmogorov-Smirnov test. Subsequently, we utilize Non-Factorized identifiable Variational Autoencoders (NF-iVAE) along with the Correlation Coefficient index (CCI) metric to identify the confounding variables within these candidate nodes. Applying our method to the Human Connectome Projects (HCP) movie-watching task data, we demonstrate that while interactions exist between dorsal and ventral regions, only dorsal regions serve as confounders for the visual networks, and vice versa. These findings align consistently with those resulting from the neuroscientific perspective. Finally, we show the reliability of our results by testing 30 independent runs for NF-iVAE initialization.

2403.05525

Haoyu Lu

Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, Chong Ruan

DeepSeek-VL: Towards Real-World Vision-Language Understanding

https://github.com/deepseek-ai/DeepSeek-VL

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.

2403.05632

Hongyi Guo

Hongyi Guo, Zhihan Liu, Yufeng Zhang, Zhaoran Wang

Can Large Language Models Play Games? A Case Study of A Self-Play Approach

cs.AI

http://creativecommons.org/licenses/by/4.0/

Large Language Models (LLMs) harness extensive data from the Internet, storing a broad spectrum of prior knowledge. While LLMs have proven beneficial as decision-making aids, their reliability is hampered by limitations in reasoning, hallucination phenomenon, and so on. On the other hand, Monte-Carlo Tree Search (MCTS) is a heuristic search algorithm that provides reliable decision-making solutions, achieved through recursive rollouts and self-play. However, the effectiveness of MCTS relies heavily on heuristic pruning and external value functions, particularly in complex decision scenarios. This work introduces an innovative approach that bolsters LLMs with MCTS self-play to efficiently resolve deterministic turn-based zero-sum games (DTZG), such as chess and go, without the need for additional training. Specifically, we utilize LLMs as both action pruners and proxies for value functions without the need for additional training. We theoretically prove that the suboptimality of the estimated value in our proposed method scales with $\tilde{\mathcal O}\Bigl(\frac{|\tilde {\mathcal A}|}{\sqrt{N}} + \epsilon_\mathrm{pruner} + \epsilon_\mathrm{critic}\Bigr)$, where \(N\) is the number of simulations, $|\tilde {\mathcal A}|$ is the cardinality of the pruned action space by LLM, and $\epsilon_\mathrm{pruner}$ and $\epsilon_\mathrm{critic}$ quantify the errors incurred by adopting LLMs as action space pruner and value function proxy, respectively. Our experiments in chess and go demonstrate the capability of our method to address challenges beyond the scope of MCTS and improve the performance of the directly application of LLMs.

2403.05801

Haotian Zheng

Chen Li, Haotian Zheng, Yiping Sun, Cangqing Wang, Liqiang Yu, Che Chang, Xinyu Tian, Bo Liu

Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques

This paper has been accepted by the 2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT 2024)

cs.AI

http://creativecommons.org/licenses/by/4.0/

In the realm of computational knowledge representation, Knowledge Graph Reasoning (KG-R) stands at the forefront of facilitating sophisticated inferential capabilities across multifarious domains. The quintessence of this research elucidates the employment of reinforcement learning (RL) strategies, notably the REINFORCE algorithm, to navigate the intricacies inherent in multi-hop KG-R. This investigation critically addresses the prevalent challenges introduced by the inherent incompleteness of Knowledge Graphs (KGs), which frequently results in erroneous inferential outcomes, manifesting as both false negatives and misleading positives. By partitioning the Unified Medical Language System (UMLS) benchmark dataset into rich and sparse subsets, we investigate the efficacy of pre-trained BERT embeddings and Prompt Learning methodologies to refine the reward shaping process. This approach not only enhances the precision of multi-hop KG-R but also sets a new precedent for future research in the field, aiming to improve the robustness and accuracy of knowledge inference within complex KG frameworks. Our work contributes a novel perspective to the discourse on KG reasoning, offering a methodological advancement that aligns with the academic rigor and scholarly aspirations of the Natural journal, promising to invigorate further advancements in the realm of computational knowledge representation.

2403.05921

Bohui Zhang

Bohui Zhang and Valentina Anita Carriero and Katrin Schreiberhuber and Stefani Tsaneva and Luc\'ia S\'anchez Gonz\'alez and Jongmo Kim and Jacopo de Berardinis

OntoChat: a Framework for Conversational Ontology Engineering using Language Models

ESWC 2024 Special Track on Large Language Models for Knowledge Engineering

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.

2403.06568

Furong Ye

Furong Ye, Chuan Luo, Shaowei Cai

Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e.g., fitness, of the best-found solutions obtained within a given running time budget. However, concerning solely the final obtained solutions regarding specific time budgets may restrict us from comprehending the behavior of the solvers along the convergence process. This paper demonstrates that Empirical Cumulative Distribution Functions can be used to compare MaxSAT local search solvers' anytime performance across multiple problem instances and various time budgets. The assessment reveals distinctions in solvers' performance and displays that the (dis)advantages of solvers adjust along different running times. This work also exhibits that the quantitative and high variance assessment of anytime performance can guide machines, i.e., automatic configurators, to search for better parameter settings. Our experimental results show that the hyperparameter optimization tool, i.e., SMAC, generally achieves better parameter settings of local search when using the anytime performance as the cost function, compared to using the fitness of the best-found solutions.

2403.06995

Lucas Maziero

Lucas Porto Maziero, F\'abio Luiz Usberti, Celso Cavellucci

Exact algorithms and heuristics for capacitated covering salesman problems

cs.AI

http://creativecommons.org/licenses/by/4.0/

This paper introduces the Capacitated Covering Salesman Problem (CCSP), approaching the notion of service by coverage in capacitated vehicle routing problems. In CCSP, locations where vehicles can transit are provided, some of which have customers with demands. The objective is to service customers through a fleet of vehicles based in a depot, minimizing the total distance traversed by the vehicles. CCSP is unique in the sense that customers, to be serviced, do not need to be visited by a vehicle. Instead, they can be serviced if they are within a coverage area of the vehicle. This assumption is motivated by applications in which some customers are unreachable (e.g., forbidden access to vehicles) or visiting every customer is impractical. In this work, optimization methodologies are proposed for the CCSP based on ILP (Integer Linear Programming) and BRKGA (Biased Random-Key Genetic Algorithm) metaheuristic. Computational experiments conducted on a benchmark of instances for the CCSP evaluate the performance of the methodologies with respect to primal bounds. Furthermore, our ILP formulation is extended in order to create a novel MILP (Mixed Integer Linear Programming) for the Multi-Depot Covering Tour Vehicle Routing Problem (MDCTVRP). Computational experiments show that the extended MILP formulation outperformed the previous state-of-the-art exact approach with respect to optimality gaps. In particular, optimal solutions were obtained for several previously unsolved instances.

2403.06996

Solve S{\ae}b{\o}

Solve S{\ae}b{\o} and Helge Brovold

On the stochastics of human and artificial creativity

40 pages, 1 figure with 2 sub-figures

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.

2403.07010

Miin-Shen Yang

Miin-Shen Yang, Yasir Akhtar, Mehboob Ali

On Globular T-Spherical Fuzzy (G-TSF) Sets with Application to G-TSF Multi-Criteria Group Decision-Making

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

In this paper, we give the concept of Globular T-Spherical Fuzzy (G-TSF) Sets (G-TSFSs) as an innovative extension of T-Spherical Fuzzy Sets (TSFSs) and Circular Spherical Fuzzy Sets (C-SFSs). G-TSFSs represent membership, indeterminacy, and non-membership degrees using a globular/sphere bound that can offer a more accurate portrayal of vague, ambiguous, and imprecise information. By employing a structured representation of data points on a sphere with a specific center and radius, this model enhances decision-making processes by enabling a more comprehensive evaluation of objects within a flexible region. Following the newly defined G-TSFSs, we establish some basic set operations and introduce fundamental algebraic operations for G-TSF Values (G-TSFVs). These operations expand the evaluative capabilities of decision-makers, facilitating more sensitive decision-making processes in a broader region. To quantify a similarity measure (SM) between GTSFVs, the SM is defined based on the radius of G-TSFSs. Additionally, Hamming distance and Euclidean distance are introduced for G-TSFSs. We also present theorems and examples to elucidate computational mechanisms. Furthermore, we give the G-TSF Weighted Average (G-TSFWA) and G-TSF Weighted Geometric (G-TSFWG) operators. Leveraging our proposed SM, a Multi-Criteria Group Decision-Making (MCGDM) scheme for G-TSFSs, named G-TSF MCGDM (G-TSFMCGDM), is developed to address group decision-making problems. The applicability and effectiveness of the proposed G-TSFMCGDM method are demonstrated by applying it to solve the selection problem of the best venue for professional development training sessions in a firm. The analysis results affirm the suitability and utility of the proposed method for resolving MCGDM problems, establishing its effectiveness in practical decision-making scenarios.

2403.07363

Yingtao Ren

Yingtao Ren, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees

IEEE Transactions on Fuzzy Systems 31.5 (2023): 1729-1741

10.1109/TFUZZ.2022.3215725

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitative and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.

2403.07510

Oliver Kim

Oliver Kim and Mohan Sridharan

Relevance Score: A Landmark-Like Heuristic for Planning

12 Pages, 3 figures

cs.AI

http://creativecommons.org/licenses/by/4.0/

Landmarks are facts or actions that appear in all valid solutions of a planning problem. They have been used successfully to calculate heuristics that guide the search for a plan. We investigate an extension to this concept by defining a novel "relevance score" that helps identify facts or actions that appear in most but not all plans to achieve any given goal. We describe an approach to compute this relevance score and use it as a heuristic in the search for a plan. We experimentally compare the performance of our approach with that of a state of the art landmark-based heuristic planning approach using benchmark planning problems. While the original landmark-based heuristic leads to better performance on problems with well-defined landmarks, our approach substantially improves performance on problems that lack non-trivial landmarks.

2403.07566

Weiwei Gu

Weiwei Gu and Senquan Wang

An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Blood Glucose (BG) control involves keeping an individual's BG within a healthy range through extracorporeal insulin injections is an important task for people with type 1 diabetes. However,traditional patient self-management is cumbersome and risky. Recent research has been devoted to exploring individualized and automated BG control approaches, among which Deep Reinforcement Learning (DRL) shows potential as an emerging approach. In this paper, we use an exponential decay model of drug concentration to convert the formalization of the BG control problem, which takes into account the delay and prolongedness of drug effects, from a PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process) to a MDP, and we propose a novel multi-step DRL-based algorithm to solve the problem. The Prioritized Experience Replay (PER) sampling method is also used in it. Compared to single-step bootstrapped updates, multi-step learning is more efficient and reduces the influence from biasing targets. Our proposed method converges faster and achieves higher cumulative rewards compared to the benchmark in the same training environment, and improves the time-in-range (TIR), the percentage of time the patient's BG is within the target range, in the evaluation phase. Our work validates the effectiveness of multi-step reinforcement learning in BG control, which may help to explore the optimal glycemic control measure and improve the survival of diabetic patients.

2403.07964

Maqsood Shah

Maqsood Hussain Shah, Yue Ding, Shaoshu Zhu, Yingqi Gu and Mingming Liu

Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services

7 pages, 3 figures, 2 tables

cs.AI

http://creativecommons.org/licenses/by/4.0/

In response to the escalating global challenge of increasing emissions and pollution in transportation, shared electric mobility services, encompassing e-cars, e-bikes, and e-scooters, have emerged as a popular strategy. However, existingshared electric mobility services exhibit critical design deficiencies, including insufficient service integration, imprecise energy consumption forecasting, limited scalability and geographical coverage, and a notable absence of a user-centric perspective, particularly in the context of multi-modal transportation. More importantly, there is no consolidated open-source framework which could benefit the e-mobility research community. This paper aims to bridge this gap by providing a pioneering open-source framework for shared e-mobility. The proposed framework, with an agent-in-the-loop approach and modular architecture, is tailored to diverse user preferences and offers enhanced customization. We demonstrate the viability of this framework by solving an integrated multi-modal route-optimization problem using the modified Ant Colony Optimization (ACO) algorithm. The primary contribution of this work is to provide a collaborative and transparent framework to tackle the dynamic challenges in the field of e-mobility research using a consolidated approach.

2403.08425

Pedro Henrique Luz de Araujo

Benjamin Roth, Pedro Henrique Luz de Araujo, Yuxi Xia, Saskia Kaltenbrunner and Christoph Korab

Specification Overfitting in Artificial Intelligence

40 pages, 2 figures

cs.AI

http://creativecommons.org/licenses/by/4.0/

Machine learning (ML) and artificial intelligence (AI) approaches are often criticized for their inherent bias and for their lack of control, accountability, and transparency. Consequently, regulatory bodies struggle with containing this technology's potential negative side effects. High-level requirements such as fairness and robustness need to be formalized into concrete specification metrics, imperfect proxies that capture isolated aspects of the underlying requirements. Given possible trade-offs between different metrics and their vulnerability to over-optimization, integrating specification metrics in system development processes is not trivial. This paper defines specification overfitting, a scenario where systems focus excessively on specified metrics to the detriment of high-level requirements and task performance. We present an extensive literature survey to categorize how researchers propose, measure, and optimize specification metrics in several AI fields (e.g., natural language processing, computer vision, reinforcement learning). Using a keyword-based search on papers from major AI conferences and journals between 2018 and mid-2023, we identify and analyze 74 papers that propose or optimize specification metrics. We find that although most papers implicitly address specification overfitting (e.g., by reporting more than one specification metric), they rarely discuss which role specification metrics should play in system development or explicitly define the scope and assumptions behind metric formulations.

2403.08843

Thi Kim Nhung Dang

Thi Kim Nhung Dang, Milan Lopuha\"a-Zwakenberg, Mari\"elle Stoelinga

Fuzzy Fault Trees Formalized

14 pages

cs.AI

http://creativecommons.org/licenses/by/4.0/

Fault tree analysis is a vital method of assessing safety risks. It helps to identify potential causes of accidents, assess their likelihood and severity, and suggest preventive measures. Quantitative analysis of fault trees is often done via the dependability metrics that compute the system's failure behaviour over time. However, the lack of precise data is a major obstacle to quantitative analysis, and so to reliability analysis. Fuzzy logic is a popular framework for dealing with ambiguous values and has applications in many domains. A number of fuzzy approaches have been proposed to fault tree analysis, but -- to the best of our knowledge -- none of them provide rigorous definitions or algorithms for computing fuzzy unreliability values. In this paper, we define a rigorous framework for fuzzy unreliability values. In addition, we provide a bottom-up algorithm to efficiently calculate fuzzy reliability for a system. The algorithm incorporates the concept of $\alpha$-cuts method. That is, performing binary algebraic operations on intervals on horizontally discretised $\alpha$-cut representations of fuzzy numbers. The method preserves the nonlinearity of fuzzy unreliability. Finally, we illustrate the results obtained from two case studies.

2403.08910

\'Angel Aso-Mollar

\'Angel Aso-Mollar, Eva Onaindia

Meta-operators for Enabling Parallel Planning Using Deep Reinforcement Learning

9 pages. Submitted to PRL workshop at ICAPS 2023

cs.AI

http://creativecommons.org/licenses/by/4.0/

There is a growing interest in the application of Reinforcement Learning (RL) techniques to AI planning with the aim to come up with general policies. Typically, the mapping of the transition model of AI planning to the state transition system of a Markov Decision Process is established by assuming a one-to-one correspondence of the respective action spaces. In this paper, we introduce the concept of meta-operator as the result of simultaneously applying multiple planning operators, and we show that including meta-operators in the RL action space enables new planning perspectives to be addressed using RL, such as parallel planning. Our research aims to analyze the performance and complexity of including meta-operators in the RL process, concretely in domains where satisfactory outcomes have not been previously achieved using usual generalized planning models. The main objective of this article is thus to pave the way towards a redefinition of the RL action space in a manner that is more closely aligned with the planning perspective.

2403.09232

Alexander Stevens

Alexander Stevens, Chun Ouyang, Johannes De Smedt, Catarina Moreira

Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes

Journal Submission

cs.AI

http://creativecommons.org/licenses/by/4.0/

In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics. Nevertheless, the inherent opacity of these algorithms poses a significant challenge for human decision-makers, hindering their ability to understand the reasoning behind the predictions. This growing concern has sparked the introduction of counterfactual explanations, designed as human-understandable what if scenarios, to provide clearer insights into the decision-making process behind undesirable predictions. The generation of counterfactual explanations, however, encounters specific challenges when dealing with the sequential nature of the (business) process cases typically used in predictive process analytics. Our paper tackles this challenge by introducing a data-driven approach, REVISEDplus, to generate more feasible and plausible counterfactual explanations. First, we restrict the counterfactual algorithm to generate counterfactuals that lie within a high-density region of the process data, ensuring that the proposed counterfactuals are realistic and feasible within the observed process data distribution. Additionally, we ensure plausibility by learning sequential patterns between the activities in the process cases, utilising Declare language templates. Finally, we evaluate the properties that define the validity of counterfactuals.

2403.09249

Imanol Echeverria

Imanol Echeverria, Maialen Murua, Roberto Santana

Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem

cs.AI

http://creativecommons.org/licenses/by/4.0/

Recent advancements in the flexible job-shop scheduling problem (FJSSP) are primarily based on deep reinforcement learning (DRL) due to its ability to generate high-quality, real-time solutions. However, DRL approaches often fail to fully harness the strengths of existing techniques such as exact methods or constraint programming (CP), which can excel at finding optimal or near-optimal solutions for smaller instances. This paper aims to integrate CP within a deep learning (DL) based methodology, leveraging the benefits of both. In this paper, we introduce a method that involves training a DL model using optimal solutions generated by CP, ensuring the model learns from high-quality data, thereby eliminating the need for the extensive exploration typical in DRL and enhancing overall performance. Further, we integrate CP into our DL framework to jointly construct solutions, utilizing DL for the initial complex stages and transitioning to CP for optimal resolution as the problem is simplified. Our hybrid approach has been extensively tested on three public FJSSP benchmarks, demonstrating superior performance over five state-of-the-art DRL approaches and a widely-used CP solver. Additionally, with the objective of exploring the application to other combinatorial optimization problems, promising preliminary results are presented on applying our hybrid approach to the traveling salesman problem, combining an exact method with a well-known DRL method.

2403.09289

Anirban Mukherjee

Anirban Mukherjee, Hannah Hanwen Chang

Silico-centric Theory of Mind

cs.AI

http://creativecommons.org/licenses/by/4.0/

Theory of Mind (ToM) refers to the ability to attribute mental states, such as beliefs, desires, intentions, and knowledge, to oneself and others, and to understand that these mental states can differ from one's own and from reality. We investigate ToM in environments with multiple, distinct, independent AI agents, each possessing unique internal states, information, and objectives. Inspired by human false-belief experiments, we present an AI ('focal AI') with a scenario where its clone undergoes a human-centric ToM assessment. We prompt the focal AI to assess whether its clone would benefit from additional instructions. Concurrently, we give its clones the ToM assessment, both with and without the instructions, thereby engaging the focal AI in higher-order counterfactual reasoning akin to human mentalizing--with respect to humans in one test and to other AI in another. We uncover a discrepancy: Contemporary AI demonstrates near-perfect accuracy on human-centric ToM assessments. Since information embedded in one AI is identically embedded in its clone, additional instructions are redundant. Yet, we observe AI crafting elaborate instructions for their clones, erroneously anticipating a need for assistance. An independent referee AI agrees with these unsupported expectations. Neither the focal AI nor the referee demonstrates ToM in our 'silico-centric' test.

2403.09361

Jin-Kao Hao

Pengfei He, Jin-Kao Hao, Qinghua Wu

A Multi-population Integrated Approach for Capacitated Location Routing

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

The capacitated location-routing problem involves determining the depots from a set of candidate capacitated depot locations and finding the required routes from the selected depots to serve a set of customers whereas minimizing a cost function that includes the cost of opening the chosen depots, the fixed utilization cost per vehicle used, and the total cost (distance) of the routes. This paper presents a multi-population integrated framework in which a multi-depot edge assembly crossover generates promising offspring solutions from the perspective of both depot location and route edge assembly. The method includes an effective neighborhood-based local search, a feasibility-restoring procedure and a diversification-oriented mutation. Of particular interest is the multi-population scheme which organizes the population into multiple subpopulations based on depot configurations. Extensive experiments on 281 benchmark instances from the literature show that the algorithm performs remarkably well, by improving 101 best-known results (new upper bounds) and matching 84 best-known results. Additional experiments are presented to gain insight into the role of the key elements of the algorithm.

2403.09404

Anirban Mukherjee

Anirban Mukherjee, Hannah Hanwen Chang

Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption

cs.AI

http://creativecommons.org/licenses/by/4.0/

Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics manifest randomly and universally. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.

2403.09481

Paloma Rabaey

Paloma Rabaey, Johannes Deleu, Stefan Heytens, Thomas Demeester

Clinical Reasoning over Tabular Data and Text with Bayesian Networks

AI in Medicine 2024

cs.AI

http://creativecommons.org/licenses/by/4.0/

Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framework. This paper compares and discusses strategies to augment Bayesian networks with neural text representations, both in a generative and discriminative manner. This is illustrated with simulation results for a primary care use case (diagnosis of pneumonia) and discussed in a broader clinical context.

2403.09806

Balaji Ganesan

Balaji Ganesan, Matheen Ahmed Pasha, Srinivasa Parkala, Neeraj R Singh, Gayatri Mishra, Sumit Bhatia, Hima Patel, Somashekar Naganna, Sameep Mehta

xLP: Explainable Link Prediction for Master Data Management

8 pages, 4 figures, NeurIPS 2020 Competition and Demonstration Track. arXiv admin note: text overlap with arXiv:2012.05516

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Explaining neural model predictions to users requires creativity. Especially in enterprise applications, where there are costs associated with users' time, and their trust in the model predictions is critical for adoption. For link prediction in master data management, we have built a number of explainability solutions drawing from research in interpretability, fact verification, path ranking, neuro-symbolic reasoning and self-explaining AI. In this demo, we present explanations for link prediction in a creative way, to allow users to choose explanations they are more comfortable with.

2403.09925

Saeid Amiri

Saeid Amiri, Parisa Zehtabi, Danial Dervovic, Michael Cashmore

Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization

Accepted to the ICAPS Planning and Scheduling for Financial Services (FINPLAN) 2023 workshop

cs.AI

http://creativecommons.org/licenses/by/4.0/

Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits. In this paper, we examine a particular class of facility location problems. Our objective is to minimize the loss of sales resulting from the removal of several retail stores. However, estimating sales accurately is expensive and time-consuming. To overcome this challenge, we leverage Monte Carlo Tree Search (MCTS) assisted by a surrogate model that computes evaluations faster. Results suggest that MCTS supported by a fast surrogate function can generate solutions faster while maintaining a consistent solution compared to MCTS that does not benefit from the surrogate function.

2403.10249

Xinrun Xu

Xinrun Xu and Yuxin Wang and Chaoyi Xu and Ziluo Ding and Jiechuan Jiang and Zhiming Ding and B\"orje F. Karlsson

A Survey on Game Playing Agents and Large Models: Methods, Applications, and Challenges

13 pages, 3 figures

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry. But despite the surge in interest in this rapidly evolving area, there are scarce systematic reviews on their capabilities and potential in distinct impactful scenarios. This paper endeavours to help bridge this gap, offering a thorough examination of the current landscape of LM usage in regards to complex game playing scenarios and the challenges still open. Here, we seek to systematically review the existing architectures of LM-based Agents (LMAs) for games and summarize their commonalities, challenges, and any other insights. Furthermore, we present our perspective on promising future research avenues for the advancement of LMs in games. We hope to assist researchers in gaining a clear understanding of the field and to generate more interest in this highly impactful research direction. A corresponding resource, continuously updated, can be found in our GitHub repository.

2403.10299

Xinrun Xu

Xinrun Xu and Zhanbiao Lian and Yurong Wu and Manying Lv and Zhiming Ding and Jian Yan and Shang Jiang

A Multi-constraint and Multi-objective Allocation Model for Emergency Rescue in IoT Environment

5 pages, 5 figures, ISCAS 2024

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Emergency relief operations are essential in disaster aftermaths, necessitating effective resource allocation to minimize negative impacts and maximize benefits. In prolonged crises or extensive disasters, a systematic, multi-cycle approach is key for timely and informed decision-making. Leveraging advancements in IoT and spatio-temporal data analytics, we've developed the Multi-Objective Shuffled Gray-Wolf Frog Leaping Model (MSGW-FLM). This multi-constraint, multi-objective resource allocation model has been rigorously tested against 28 diverse challenges, showing superior performance in comparison to established models such as NSGA-II, IBEA, and MOEA/D. MSGW-FLM's effectiveness is particularly notable in complex, multi-cycle emergency rescue scenarios, which involve numerous constraints and objectives. This model represents a significant step forward in optimizing resource distribution in emergency response situations.

2403.10415

Yongjie Wang

Yongjie Wang, Tong Zhang, Xu Guo and Zhiqi Shen

Gradient based Feature Attribution in Explainable AI: A Technical Review

cs.AI

http://creativecommons.org/licenses/by/4.0/

The surge in black-box AI models has prompted the need to explain the internal mechanism and justify their reliability, especially in high-stakes applications, such as healthcare and autonomous driving. Due to the lack of a rigorous definition of explainable AI (XAI), a plethora of research related to explainability, interpretability, and transparency has been developed to explain and analyze the model from various perspectives. Consequently, with an exhaustive list of papers, it becomes challenging to have a comprehensive overview of XAI research from all aspects. Considering the popularity of neural networks in AI research, we narrow our focus to a specific area of XAI research: gradient based explanations, which can be directly adopted for neural network models. In this review, we systematically explore gradient based explanation methods to date and introduce a novel taxonomy to categorize them into four distinct classes. Then, we present the essence of technique details in chronological order and underscore the evolution of algorithms. Next, we introduce both human and quantitative evaluations to measure algorithm performance. More importantly, we demonstrate the general challenges in XAI and specific challenges in gradient based explanations. We hope that this survey can help researchers understand state-of-the-art progress and their corresponding disadvantages, which could spark their interest in addressing these issues in future work.

2403.10502

Giovanni Casini

Umberto Straccia, Giovanni Casini

Belief Change based on Knowledge Measures

48 pages, 3 figures, preprint

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Knowledge Measures (KMs) aim at quantifying the amount of knowledge/information that a knowledge base carries. On the other hand, Belief Change (BC) is the process of changing beliefs (in our case, in terms of contraction, expansion and revision) taking into account a new piece of knowledge, which possibly may be in contradiction with the current belief. We propose a new quantitative BC framework that is based on KMs by defining belief change operators that try to minimise, from an information-theoretic point of view, the surprise that the changed belief carries. To this end, we introduce the principle of minimal surprise. In particular, our contributions are (i) a general information-theoretic approach to KMs for which [1] is a special case; (ii) KM-based BC operators that satisfy the so-called AGM postulates; and (iii) a characterisation of any BC operator that satisfies the AGM postulates as a KM-based BC operator, i.e., any BC operator satisfying the AGM postulates can be encoded within our quantitative BC framework. We also introduce quantitative measures that account for the information loss of contraction, information gain of expansion and information change of revision. We also give a succinct look into the problem of iterated revision, which deals with the application of a sequence of revision operations in our framework, and also illustrate how one may build from our KM-based contraction operator also one not satisfying the (in)famous recovery postulate, by focusing on the so-called severe withdrawal model as an illustrative example.

2403.10720

Ye Zhang

Ye Zhang, Mengran Zhu, Kailin Gui, Jiayue Yu, Yong Hao, Haozhan Sun

Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies

This paper has been accepted by CVIDL2024

cs.AI

http://creativecommons.org/licenses/by/4.0/

In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted. Notwithstanding its broad applicability, the algorithm's performance can be adversely impacted in certain scenarios, particularly within the domain of game strategy development. This research posits that the inherent branch divergence within the Da Vinci Code board game significantly impedes parallelism when executed on Graphics Processing Units (GPUs). To investigate this hypothesis, we implemented and meticulously evaluated two variants of the MCTS algorithm, specifically designed to assess the impact of branch divergence on computational performance. Our comparative analysis reveals a linear improvement in performance with the CPU-based implementation, in stark contrast to the GPU implementation, which exhibits a non-linear enhancement pattern and discernible performance troughs. These findings contribute to a deeper understanding of the MCTS algorithm's behavior in divergent branch scenarios, highlighting critical considerations for optimizing game strategy algorithms on parallel computing architectures.

2403.10744

Zhiyi Tan

Zhiyi Tan, Bingkun Bao

Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control

16 pages, single line, 7 figures, written with Springer conference template

cs.AI

http://creativecommons.org/licenses/by/4.0/

In recent years, epidemic policy-making models are increasingly being used to provide reference for governors on prevention and control policies against catastrophic epidemics such as SARS, H1N1 and COVID-19. Existing studies are currently constrained by two issues: First, previous methods develop policies based on effect evaluation, since few of factors in real-world decision-making can be modeled, the output policies will then easily become extreme. Second, the subjectivity and cognitive limitation of human make the historical policies not always optimal for the training of decision models. To these ends, we present a novel Policy Combination Synthesis (PCS) model for epidemic policy-making. Specially, to prevent extreme decisions, we introduce adversarial learning between the model-made policies and the real policies to force the output policies to be more human-liked. On the other hand, to minimize the impact of sub-optimal historical policies, we employ contrastive learning to let the model draw on experience from the best historical policies under similar scenarios. Both adversarial and contrastive learning are adaptive based on the comprehensive effects of real policies to ensure the model always learns useful information. Extensive experiments on real-world data prove the effectiveness of the proposed model.

2403.10930

Yifeng Zeng

Huifan Gao, Yifeng Zeng and Yinghui Pan

Inducing Individual Students' Learning Strategies through Homomorphic POMDPs

11pages, 3figures

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Optimizing students' learning strategies is a crucial component in intelligent tutoring systems. Previous research has demonstrated the effectiveness of devising personalized learning strategies for students by modelling their learning processes through partially observable Markov decision process (POMDP). However, the research holds the assumption that the student population adheres to a uniform cognitive pattern. While this assumption simplifies the POMDP modelling process, it evidently deviates from a real-world scenario, thus reducing the precision of inducing individual students' learning strategies. In this article, we propose the homomorphic POMDP (H-POMDP) model to accommodate multiple cognitive patterns and present the parameter learning approach to automatically construct the H-POMDP model. Based on the H-POMDP model, we are able to represent different cognitive patterns from the data and induce more personalized learning strategies for individual students. We conduct experiments to show that, in comparison to the general POMDP approach, the H-POMDP model demonstrates better precision when modelling mixed data from multiple cognitive patterns. Moreover, the learning strategies derived from H-POMDPs exhibit better personalization in the performance evaluation.

2403.11219

Abraham Itzhak Weinberg

Abraham Itzhak Weinberg, Cristiano Premebida, Diego Resende Faria

Causality from Bottom to Top: A Survey

cs.AI

http://creativecommons.org/licenses/by-sa/4.0/

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.

2403.12308

Chao Chen

Chao Chen, Christian Wagner, Jonathan M. Garibaldi

Gradient-based Fuzzy System Optimisation via Automatic Differentiation -- FuzzyR as a Use Case

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Since their introduction, fuzzy sets and systems have become an important area of research known for its versatility in modelling, knowledge representation and reasoning, and increasingly its potential within the context explainable AI. While the applications of fuzzy systems are diverse, there has been comparatively little advancement in their design from a machine learning perspective. In other words, while representations such as neural networks have benefited from a boom in learning capability driven by an increase in computational performance in combination with advances in their training mechanisms and available tool, in particular gradient descent, the impact on fuzzy system design has been limited. In this paper, we discuss gradient-descent-based optimisation of fuzzy systems, focussing in particular on automatic differentiation -- crucial to neural network learning -- with a view to free fuzzy system designers from intricate derivative computations, allowing for more focus on the functional and explainability aspects of their design. As a starting point, we present a use case in FuzzyR which demonstrates how current fuzzy inference system implementations can be adjusted to leverage powerful features of automatic differentiation tools sets, discussing its potential for the future of fuzzy system design.

2403.12451

Lirui Luo

Lirui Luo, Guoxi Zhang, Hongming Xu, Yaodong Yang, Cong Fang, Qing Li

INSIGHT: End-to-End Neuro-Symbolic Visual Reinforcement Learning with Language Explanations

ICML 2024. Project page: https://ins-rl.github.io/

cs.AI

http://creativecommons.org/licenses/by/4.0/

Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies. NS-RL entails structured state representations for tasks with visual observations, but previous methods are unable to refine the structured states with rewards due to a lack of efficiency. Accessibility also remains to be an issue, as extensive domain knowledge is required to interpret symbolic policies. In this paper, we present a framework for learning structured states and symbolic policies jointly, whose key idea is to distill vision foundation models into a scalable perception module and refine it during policy learning. Moreover, we design a pipeline to generate language explanations for policies and decisions using large language models. In experiments on nine Atari tasks, we verify the efficacy of our approach, and we also present explanations for policies and decisions.

2403.13705

Aske Plaat

Aske Plaat

Research Re: search & Re-search

PhD thesis Aske Plaat 20 June 1996. AlphaBeta, SSS*, MTD(f)

cs.AI

http://creativecommons.org/licenses/by/4.0/

Search algorithms are often categorized by their node expansion strategy. One option is the depth-first strategy, a simple backtracking strategy that traverses the search space in the order in which successor nodes are generated. An alternative is the best-first strategy, which was designed to make it possible to use domain-specific heuristic information. By exploring promising parts of the search space first, best-first algorithms are usually more efficient than depth-first algorithms. In programs that play minimax games such as chess and checkers, the efficiency of the search is of crucial importance. Given the success of best-first algorithms in other domains, one would expect them to be used for minimax games too. However, all high-performance game-playing programs are based on a depth-first algorithm. This study takes a closer look at a depth-first algorithm, AB, and a best-first algorithm, SSS. The prevailing opinion on these algorithms is that SSS offers the potential for a more efficient search, but that its complicated formulation and exponential memory requirements render it impractical. The theoretical part of this work shows that there is a surprisingly straightforward link between the two algorithms -- for all practical purposes, SSS is a special case of AB. Subsequent empirical evidence proves the prevailing opinion on SSS to be wrong: it is not a complicated algorithm, it does not need too much memory, and it is also not more efficient than depth-first search.

2403.14100

Steven Mascaro

Steven Mascaro, Yue Wu, Ross Pearson, Owen Woodberry, Jessica Ramsay, Tom Snelling, Ann E. Nicholson

Causal knowledge engineering: A case study from COVID-19

22 pages (plus 19 pages in appendices), 9 figures, submitted for review

cs.AI

http://creativecommons.org/licenses/by/4.0/

COVID-19 appeared abruptly in early 2020, requiring a rapid response amid a context of great uncertainty. Good quality data and knowledge was initially lacking, and many early models had to be developed with causal assumptions and estimations built in to supplement limited data, often with no reliable approach for identifying, validating and documenting these causal assumptions. Our team embarked on a knowledge engineering process to develop a causal knowledge base consisting of several causal BNs for diverse aspects of COVID-19. The unique challenges of the setting lead to experiments with the elicitation approach, and what emerged was a knowledge engineering method we call Causal Knowledge Engineering (CKE). The CKE provides a structured approach for building a causal knowledge base that can support the development of a variety of application-specific models. Here we describe the CKE method, and use our COVID-19 work as a case study to provide a detailed discussion and analysis of the method.

2403.14796

Erez Karpas

Andrew Coles, Erez Karpas, Andrey Lavrinenko, Wheeler Ruml, Solomon Eyal Shimony, Shahaf Shperberg

Planning and Acting While the Clock Ticks

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Standard temporal planning assumes that planning takes place offline and then execution starts at time 0. Recently, situated temporal planning was introduced, where planning starts at time 0 and execution occurs after planning terminates. Situated temporal planning reflects a more realistic scenario where time passes during planning. However, in situated temporal planning a complete plan must be generated before any action is executed. In some problems with time pressure, timing is too tight to complete planning before the first action must be executed. For example, an autonomous car that has a truck backing towards it should probably move out of the way now and plan how to get to its destination later. In this paper, we propose a new problem setting: concurrent planning and execution, in which actions can be dispatched (executed) before planning terminates. Unlike previous work on planning and execution, we must handle wall clock deadlines that affect action applicability and goal achievement (as in situated planning) while also supporting dispatching actions before a complete plan has been found. We extend previous work on metareasoning for situated temporal planning to develop an algorithm for this new setting. Our empirical evaluation shows that when there is strong time pressure, our approach outperforms situated temporal planning.

2403.15251

Argaman Mordoch

Argaman Mordoch, Enrico Scala, Roni Stern, Brendan Juba

Safe Learning of PDDL Domains with Conditional Effects -- Extended Version

cs.AI

http://creativecommons.org/licenses/by/4.0/

Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Manually designing such an action model is a notoriously challenging task. An alternative is to automatically learn action models from observation. Such an action model is called safe if every plan created with it is consistent with the real, unknown action model. Algorithms for learning such safe action models exist, yet they cannot handle domains with conditional or universal effects, which are common constructs in many planning problems. We prove that learning non-trivial safe action models with conditional effects may require an exponential number of samples. Then, we identify reasonable assumptions under which such learning is tractable and propose SAM Learning of Conditional Effects (Conditional-SAM), the first algorithm capable of doing so. We analyze Conditional-SAM theoretically and evaluate it experimentally. Our results show that the action models learned by Conditional-SAM can be used to solve perfectly most of the test set problems in most of the experimented domains.

2403.15297

Tiansi Dong

Tiansi Dong, Mateja Jamnik, Pietro Li\`o

Sphere Neural-Networks for Rational Reasoning

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

The success of Large Language Models (LLMs), e.g., ChatGPT, is witnessed by their planetary popularity, their capability of human-like question-answering, and also by their steadily improved reasoning performance. However, it remains unclear whether LLMs reason. It is an open problem how traditional neural networks can be qualitatively extended to go beyond the statistic paradigm and achieve high-level cognition. Here, we present a minimalist qualitative extension by generalising computational building blocks from vectors to spheres. We propose Sphere Neural Networks (SphNNs) for human-like reasoning through model construction and inspection, and develop SphNN for syllogistic reasoning, a microcosm of human rationality. Instead of training data, SphNN uses a neuro-symbolic transition map of neighbourhood spatial relations to guide transformations from the current sphere configuration towards the target. SphNN is the first neural model that can determine the validity of long-chained syllogistic reasoning in one epoch by constructing sphere configurations as Euler diagrams, with the worst computational complexity of O(N^2). SphNN can evolve into various types of reasoning, such as spatio-temporal reasoning, logical reasoning with negation and disjunction, event reasoning, neuro-symbolic reasoning, and humour understanding (the highest level of cognition). All these suggest a new kind of Herbert A. Simon's scissors with two neural blades. SphNNs will tremendously enhance interdisciplinary collaborations to develop the two neural blades and realise deterministic neural reasoning and human-bounded rationality and elevate LLMs to reliable psychological AI. This work suggests that the non-zero radii of spheres are the missing components that prevent traditional deep-learning systems from reaching the realm of rational reasoning and cause LLMs to be trapped in the swamp of hallucination.

2403.15574

Yuhan Xia

Yuhan Xia, Qingqing Zhao, Yunfei Long, Ge Xu and Jia Wang

SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification

Accepted by CogALex 2024 conference

cs.AI

http://creativecommons.org/licenses/by-sa/4.0/

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neuro-cognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5's attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5's success signifies a pivotal change in the NLP domain, highlighting the potential influence of neuro-cognitive data in refining machine learning models' emotional sensitivity.

2403.15586

Aashish Ghimire

Aashish Ghimire, James Prather and John Edwards

Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.

2403.15587

Cristina Zuheros

Cristina Zuheros and David Herrera-Poyatos and Rosana Montes and Francisco Herrera

Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Social Media and Internet have the potential to be exploited as a source of opinion to enrich Decision Making solutions. Crowd Decision Making (CDM) is a methodology able to infer opinions and decisions from plain texts, such as reviews published in social media platforms, by means of Sentiment Analysis. Currently, the emergence and potential of Large Language Models (LLMs) lead us to explore new scenarios of automatically understand written texts, also known as natural language processing. This paper analyzes the use of ChatGPT based on prompt design strategies to assist in CDM processes to extract opinions and make decisions. We integrate ChatGPT in CDM processes as a flexible tool that infer the opinions expressed in texts, providing numerical or linguistic evaluations where the decision making models are based on the prompt design strategies. We include a multi-criteria decision making scenario with a category ontology for criteria. We also consider ChatGPT as an end-to-end CDM model able to provide a general opinion and score on the alternatives. We conduct empirical experiments on real data extracted from TripAdvisor, the TripR-2020Large dataset. The analysis of results show a promising branch for developing quality decision making models using ChatGPT. Finally, we discuss the challenges of consistency, sensitivity and explainability associated to the use of LLMs in CDM processes, raising open questions for future studies.

2403.15640

Xin Chen

Xin Chen, I-Hong Hou

Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model both the internal state transitions of each arm and the influence of external global environmental contexts. Using the dual decomposition method, we develop a scalable index policy algorithm for solving the CRB problem, and theoretically analyze the asymptotical optimality of this algorithm. In the case when the arm models are unknown, we further propose a model-based online learning algorithm based on the index policy to learn the arm models and make decisions simultaneously. Furthermore, we apply the proposed CRB framework and the index policy algorithm specifically to the demand response decision-making problem in smart grids. The numerical simulations demonstrate the performance and efficiency of our proposed CRB approaches.

2403.15728

Ruijie Liu

Ruijie Liu, Tianxiang Zhan, Zhen Li, Yong Deng

Learnable WSN Deployment of Evidential Collaborative Sensing Model

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In wireless sensor networks (WSNs), coverage and deployment are two most crucial issues when conducting detection tasks. However, the detection information collected from sensors is oftentimes not fully utilized and efficiently integrated. Such sensing model and deployment strategy, thereby, cannot reach the maximum quality of coverage, particularly when the amount of sensors within WSNs expands significantly. In this article, we aim at achieving the optimal coverage quality of WSN deployment. We develop a collaborative sensing model of sensors to enhance detection capabilities of WSNs, by leveraging the collaborative information derived from the combination rule under the framework of evidence theory. In this model, the performance evaluation of evidential fusion systems is adopted as the criterion of the sensor selection. A learnable sensor deployment network (LSDNet) considering both sensor contribution and detection capability, is proposed for achieving the optimal deployment of WSNs. Moreover, we deeply investigate the algorithm for finding the requisite minimum number of sensors that realizes the full coverage of WSNs. A series of numerical examples, along with an application of forest area monitoring, are employed to demonstrate the effectiveness and the robustness of the proposed algorithms.

2403.15779

Youyang Qu

Youyang Qu, Ming Ding, Nan Sun, Kanchana Thilakarathna, Tianqing Zhu, Dusit Niyato

The Frontier of Data Erasure: Machine Unlearning for Large Language Models

cs.AI

http://creativecommons.org/licenses/by/4.0/

Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information from their vast datasets. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns, offering techniques for LLMs to selectively discard certain data. This paper reviews the latest in machine unlearning for LLMs, introducing methods for the targeted forgetting of information to address privacy, ethical, and legal challenges without necessitating full model retraining. It divides existing research into unlearning from unstructured/textual data and structured/classification data, showcasing the effectiveness of these approaches in removing specific data while maintaining model efficacy. Highlighting the practicality of machine unlearning, this analysis also points out the hurdles in preserving model integrity, avoiding excessive or insufficient data removal, and ensuring consistent outputs, underlining the role of machine unlearning in advancing responsible, ethical AI.

2403.15864

Yihang Zhao

Yihang Zhao, Neil Vetter, Kaveh Aryan

Using Large Language Models for OntoClean-based Ontology Refinement

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints. Manually conducting the first step proves difficult in practice, due to the need for philosophical expertise and lack of consensus among ontologists. By employing LLMs with two prompting strategies, the study demonstrates that high accuracy in the labelling process can be achieved. The findings suggest the potential for LLMs to enhance ontology refinement, proposing the development of plugin software for ontology tools to facilitate this integration.

2403.15879

Gyubok Lee

Gyubok Lee, Woosog Chay, Seonhee Cho, Edward Choi

TrustSQL: A Reliability Benchmark for Text-to-SQL Models with Diverse Unanswerable Questions

under review

cs.AI

http://creativecommons.org/licenses/by/4.0/

Recent advances in large language models (LLMs) have led to significant improvements in translating natural language questions into SQL queries. While achieving high accuracy in SQL generation is crucial, little is known about the extent to which these text-to-SQL models can reliably handle diverse types of questions encountered during real-world deployment, including unanswerable ones. To explore this aspect, we introduce TrustSQL, a new benchmark designed to assess the reliability of text-to-SQL models in both single-database and cross-database settings. TrustSQL requires models to provide one of two outputs: 1) an SQL prediction or 2) abstention from making an SQL prediction, either due to potential errors in the generated SQL or when faced with unanswerable questions. For model evaluation, we explore various modeling approaches specifically designed for this task: 1) optimizing separate models for answerability detection, SQL generation, and error detection, which are then integrated into a single pipeline; and 2) developing a unified approach that uses a single model to solve this task. Experimental results using our new reliability score show that addressing this challenge involves many different areas of research and opens new avenues for model development. However, none of the methods consistently surpasses the reliability scores of a naive baseline that abstains from SQL predictions for all questions, with varying penalties.

2403.15916

Alexandros Nikou PhD

Albin Larsson Forsberg and Alexandros Nikou and Aneta Vulgarakis Feljan and Jana Tumova

Multi-agent transformer-accelerated RL for satisfaction of STL specifications

Submitted to L4DC 2024 conference

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases. This issue is further exacerbated if the problem considered is temporally dependent. State-of-the-art solutions today mainly follow centralized training with decentralized execution paradigm in order to handle the scalability concerns. In this paper, we propose time-dependent multi-agent transformers which can solve the temporally dependent multi-agent problem efficiently with a centralized approach via the use of transformers that proficiently handle the large input. We highlight the efficacy of this method on two problems and use tools from statistics to verify the probability that the trajectories generated under the policy satisfy the task. The experiments show that our approach has superior performance against the literature baseline algorithms in both cases.

2403.16066

Youngbin Lee

Yejin Kim, Youngbin Lee, Vincent Yuan, Annika Lee, Yongjae Lee

A Temporal Graph Network Framework for Dynamic Recommendation

Presented at the AAAI 2024 Workshop on Recommendation Ecosystems: Modeling, Optimization and Incentive Design

cs.AI

http://creativecommons.org/licenses/by/4.0/

Recommender systems, crucial for user engagement on platforms like e-commerce and streaming services, often lag behind users' evolving preferences due to static data reliance. After Temporal Graph Networks (TGNs) were proposed, various studies have shown that TGN can significantly improve situations where the features of nodes and edges dynamically change over time. However, despite its promising capabilities, it has not been directly applied in recommender systems to date. Our study bridges this gap by directly implementing Temporal Graph Networks (TGN) in recommender systems, a first in this field. Using real-world datasets and a range of graph and history embedding methods, we show TGN's adaptability, confirming its effectiveness in dynamic recommendation scenarios.

2403.16100

Louise Dennis Dr

Louise A. Dennis and Michael Fisher

Specifying Agent Ethics (Blue Sky Ideas)

To appear in Coordination, Organizations, Institutions, Norms and Ethics for Governance of Multi-Agent Systems 2024

cs.AI

http://creativecommons.org/licenses/by/4.0/

We consider the question of what properties a Machine Ethics system should have. This question is complicated by the existence of ethical dilemmas with no agreed upon solution. We provide an example to motivate why we do not believe falling back on the elicitation of values from stakeholders is sufficient to guarantee correctness of such systems. We go on to define two broad categories of ethical property that have arisen in our own work and present a challenge to the community to approach this question in a more systematic way.

2403.16101

Yuya Sasaki

Yuya Sasaki, Sohei Tokuno, Haruka Maeda, Osamu Sakura

Evaluating Fairness Metrics Across Borders from Human Perceptions

cs.AI

http://creativecommons.org/licenses/by/4.0/

Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with established fairness metrics. Several surveys have been conducted to evaluate fairness metrics with human perceptions of fairness. However, these surveys were limited in scope, including only a few hundred participants within a single country. In this study, we conduct an international survey to evaluate the appropriateness of various fairness metrics in decision-making scenarios. We collected responses from 1,000 participants in each of China, France, Japan, and the United States, amassing a total of 4,000 responses, to analyze the preferences of fairness metrics. Our survey consists of three distinct scenarios paired with four fairness metrics, and each participant answers their preference for the fairness metric in each case. This investigation explores the relationship between personal attributes and the choice of fairness metrics, uncovering a significant influence of national context on these preferences.

2403.16162

Lu Bai

Lu Bai, Abhishek Gupta, and Yew-Soon Ong

Multi-Task Learning with Multi-Task Optimization

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Multi-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading to performance trade-offs. To arrive at a set of optimized yet well-distributed models that collectively embody different trade-offs in one algorithmic pass, this paper proposes to view Pareto multi-task learning through the lens of multi-task optimization. Multi-task learning is first cast as a multi-objective optimization problem, which is then decomposed into a diverse set of unconstrained scalar-valued subproblems. These subproblems are solved jointly using a novel multi-task gradient descent method, whose uniqueness lies in the iterative transfer of model parameters among the subproblems during the course of optimization. A theorem proving faster convergence through the inclusion of such transfers is presented. We investigate the proposed multi-task learning with multi-task optimization for solving various problem settings including image classification, scene understanding, and multi-target regression. Comprehensive experiments confirm that the proposed method significantly advances the state-of-the-art in discovering sets of Pareto-optimized models. Notably, on the large image dataset we tested on, namely NYUv2, the hypervolume convergence achieved by our method was found to be nearly two times faster than the next-best among the state-of-the-art.

2403.16206

Yuxin Qiao

Tianrui Liu, Qi Cai, Changxin Xu, Bo Hong, Fanghao Ni, Yuxin Qiao, and Tsungwei Yang

Rumor Detection with a novel graph neural network approach

10 pages, 5 figures

cs.AI

http://creativecommons.org/licenses/by/4.0/

The wide spread of rumors on social media has caused a negative impact on people's daily life, leading to potential panic, fear, and mental health problems for the public. How to debunk rumors as early as possible remains a challenging problem. Existing studies mainly leverage information propagation structure to detect rumors, while very few works focus on correlation among users that they may coordinate to spread rumors in order to gain large popularity. In this paper, we propose a new detection model, that jointly learns both the representations of user correlation and information propagation to detect rumors on social media. Specifically, we leverage graph neural networks to learn the representations of user correlation from a bipartite graph that describes the correlations between users and source tweets, and the representations of information propagation with a tree structure. Then we combine the learned representations from these two modules to classify the rumors. Since malicious users intend to subvert our model after deployment, we further develop a greedy attack scheme to analyze the cost of three adversarial attacks: graph attack, comment attack, and joint attack. Evaluation results on two public datasets illustrate that the proposed MODEL outperforms the state-of-the-art rumor detection models. We also demonstrate our method performs well for early rumor detection. Moreover, the proposed detection method is more robust to adversarial attacks compared to the best existing method. Importantly, we show that it requires a high cost for attackers to subvert user correlation pattern, demonstrating the importance of considering user correlation for rumor detection.

2403.16222

Manish Bhattarai

Ryan Barron, Maksim E. Eren, Manish Bhattarai, Selma Wanna, Nicholas Solovyev, Kim Rasmussen, Boian S. Alexandrov, Charles Nicholas, Cynthia Matuszek

Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization

Accepted at IEEE ISDFS

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG.

2403.16289

Ali Nouri

Ali Nouri, Beatriz Cabrero-Daniel, Fredrik T\"orner, H\.akan Sivencrona, Christian Berger

Engineering Safety Requirements for Autonomous Driving with Large Language Models

Accepted in 32nd IEEE International Requirements Engineering 2024 conference, Iceland

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Changes and updates in the requirement artifacts, which can be frequent in the automotive domain, are a challenge for SafetyOps. Large Language Models (LLMs), with their impressive natural language understanding and generating capabilities, can play a key role in automatically refining and decomposing requirements after each update. In this study, we propose a prototype of a pipeline of prompts and LLMs that receives an item definition and outputs solutions in the form of safety requirements. This pipeline also performs a review of the requirement dataset and identifies redundant or contradictory requirements. We first identified the necessary characteristics for performing HARA and then defined tests to assess an LLM's capability in meeting these criteria. We used design science with multiple iterations and let experts from different companies evaluate each cycle quantitatively and qualitatively. Finally, the prototype was implemented at a case company and the responsible team evaluated its efficiency.

2403.16416

Lixi Zhu

Lixi Zhu, Xiaowen Huang, Jitao Sang

How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Conversational Recommender System (CRS) interacts with users through natural language to understand their preferences and provide personalized recommendations in real-time. CRS has demonstrated significant potential, prompting researchers to address the development of more realistic and reliable user simulators as a key focus. Recently, the capabilities of Large Language Models (LLMs) have attracted a lot of attention in various fields. Simultaneously, efforts are underway to construct user simulators based on LLMs. While these works showcase innovation, they also come with certain limitations that require attention. In this work, we aim to analyze the limitations of using LLMs in constructing user simulators for CRS, to guide future research. To achieve this goal, we conduct analytical validation on the notable work, iEvaLM. Through multiple experiments on two widely-used datasets in the field of conversational recommendation, we highlight several issues with the current evaluation methods for user simulators based on LLMs: (1) Data leakage, which occurs in conversational history and the user simulator's replies, results in inflated evaluation results. (2) The success of CRS recommendations depends more on the availability and quality of conversational history than on the responses from user simulators. (3) Controlling the output of the user simulator through a single prompt template proves challenging. To overcome these limitations, we propose SimpleUserSim, employing a straightforward strategy to guide the topic toward the target items. Our study validates the ability of CRS models to utilize the interaction information, significantly improving the recommendation results.

2403.16427

Ziyan Wang

Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua, Kaidong Feng, Wenya Wang, Jie Zhang

Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation

11 pages, 4 figures

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been widely investigated to align LLMs with SBR. However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations. Although the latter methods attempt to fine-tune LLMs with domain-specific knowledge, they face limitations such as high computational costs and reliance on open-source backbones. To address such issues, we propose a Reflective Reinforcement Large Language Model (Re2LLM) for SBR, guiding LLMs to focus on specialized knowledge essential for more accurate recommendations effectively and efficiently. In particular, we first design the Reflective Exploration Module to effectively extract knowledge that is readily understandable and digestible by LLMs. To be specific, we direct LLMs to examine recommendation errors through self-reflection and construct a knowledge base (KB) comprising hints capable of rectifying these errors. To efficiently elicit the correct reasoning of LLMs, we further devise the Reinforcement Utilization Module to train a lightweight retrieval agent. It learns to select hints from the constructed KB based on the task-specific feedback, where the hints can serve as guidance to help correct LLMs reasoning for better recommendations. Extensive experiments on multiple real-world datasets demonstrate that our method consistently outperforms state-of-the-art methods.

2403.16501

Debodeep Banerjee

Debodeep Banerjee, Stefano Teso, Burcu Sayin, Andrea Passerini

Learning To Guide Human Decision Makers With Vision-Language Models

cs.AI

http://creativecommons.org/licenses/by-nc-sa/4.0/

There is increasing interest in developing AIs for assisting human decision-making in high-stakes tasks, such as medical diagnosis, for the purpose of improving decision quality and reducing cognitive strain. Mainstream approaches team up an expert with a machine learning model to which safer decisions are offloaded, thus letting the former focus on cases that demand their attention. his separation of responsibilities setup, however, is inadequate for high-stakes scenarios. On the one hand, the expert may end up over-relying on the machine's decisions due to anchoring bias, thus losing the human oversight that is increasingly being required by regulatory agencies to ensure trustworthy AI. On the other hand, the expert is left entirely unassisted on the (typically hardest) decisions on which the model abstained. As a remedy, we introduce learning to guide (LTG), an alternative framework in which - rather than taking control from the human expert - the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision. In order to ensure guidance is interpretable} and task-specific, we develop SLOG, an approach for turning any vision-language model into a capable generator of textual guidance by leveraging a modicum of human feedback. Our empirical evaluation highlights the promise of \method on a challenging, real-world medical diagnosis task.

2403.16508

Dillon Z. Chen

Dillon Z. Chen, Felipe Trevizan, Sylvie Thi\'ebaux

Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning

Extended version of ICAPS 2024 paper

cs.AI

http://creativecommons.org/licenses/by/4.0/

Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learning methods which have up to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude faster than the state-of-the-art deep learning for planning models. Our novel approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the $h^{\text{FF}}$ heuristic in a fair competition setting. It also outperforms or ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on plan quality. WL-GOOSE is the first learning for planning model which achieves these feats. Furthermore, we study the connections between our novel WL feature generation method, previous theoretically flavoured learning architectures, and Description Logic Features for planning.

2403.16524

Bastin Tony Roy Savarimuthu

Bastin Tony Roy Savarimuthu, Surangika Ranathunga, Stephen Cranefield

Harnessing the power of LLMs for normative reasoning in MASs

12 pages, 1 figure, accepted to COINE 2024 workshop at AAMAS 2024 (https://coin-workshop.github.io/coine-2024-auckland/accepted_papers.html)

cs.AI

http://creativecommons.org/licenses/by/4.0/

Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to create socially aware agents. However, traditional techniques have limitations, such as operating in limited environments often using brittle symbolic reasoning. The advent of Large Language Models (LLMs) offers a promising solution, providing a rich and expressive vocabulary for norms and enabling norm-capable agents that can perform a range of tasks such as norm discovery, normative reasoning and decision-making. This paper examines the potential of LLM-based agents to acquire normative capabilities, drawing on recent Natural Language Processing (NLP) and LLM research. We present our vision for creating normative LLM agents. In particular, we discuss how the recently proposed "LLM agent" approaches can be extended to implement such normative LLM agents. We also highlight challenges in this emerging field. This paper thus aims to foster collaboration between MAS, NLP and LLM researchers in order to advance the field of normative agents.