arXiv - CS - Artificial Intelligence最新文献

Abductive explanations of classifiers under constraints: Complexity and properties 约束条件下分类器的归纳解释：复杂性与特性

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-18 DOI: arxiv-2409.12154

Martin Cooper, Leila Amgoud

引用次数: 0

Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach 利用证据权重 (WoE) 实现可解释的目标识别：以人为本的方法

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-18 DOI: arxiv-2409.11675

Abeer Alshehri, Amal Abdulrahman, Hajar Alamri, Tim Miller, Mor Vered

{"title":"Towards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach","authors":"Abeer Alshehri, Amal Abdulrahman, Hajar Alamri, Tim Miller, Mor Vered","doi":"arxiv-2409.11675","DOIUrl":"https://doi.org/arxiv-2409.11675","url":null,"abstract":"Goal recognition (GR) involves inferring an agent's unobserved goal from a\u0000sequence of observations. This is a critical problem in AI with diverse\u0000applications. Traditionally, GR has been addressed using 'inference to the best\u0000explanation' or abduction, where hypotheses about the agent's goals are\u0000generated as the most plausible explanations for observed behavior.\u0000Alternatively, some approaches enhance interpretability by ensuring that an\u0000agent's behavior aligns with an observer's expectations or by making the\u0000reasoning behind decisions more transparent. In this work, we tackle a\u0000different challenge: explaining the GR process in a way that is comprehensible\u0000to humans. We introduce and evaluate an explainable model for goal recognition\u0000(GR) agents, grounded in the theoretical framework and cognitive processes\u0000underlying human behavior explanation. Drawing on insights from two human-agent\u0000studies, we propose a conceptual framework for human-centered explanations of\u0000GR. Using this framework, we develop the eXplainable Goal Recognition (XGR)\u0000model, which generates explanations for both why and why not questions. We\u0000evaluate the model computationally across eight GR benchmarks and through three\u0000user studies. The first study assesses the efficiency of generating human-like\u0000explanations within the Sokoban game domain, the second examines perceived\u0000explainability in the same domain, and the third evaluates the model's\u0000effectiveness in aiding decision-making in illegal fishing detection. Results\u0000demonstrate that the XGR model significantly enhances user understanding,\u0000trust, and decision-making compared to baseline models, underscoring its\u0000potential to improve human-agent collaboration.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models 用简单 SIR 模型解决大流行规划问题的度量混合规划方法

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-18 DOI: arxiv-2409.11631

Ari Gestetner, Buser Say

引用次数: 0

Explaining Non-monotonic Normative Reasoning using Argumentation Theory with Deontic Logic 用论证理论与 Deontic Logic 解释非单调规范推理

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-18 DOI: arxiv-2409.11780

Zhe Yu, Yiwei Lu

{"title":"Explaining Non-monotonic Normative Reasoning using Argumentation Theory with Deontic Logic","authors":"Zhe Yu, Yiwei Lu","doi":"arxiv-2409.11780","DOIUrl":"https://doi.org/arxiv-2409.11780","url":null,"abstract":"In our previous research, we provided a reasoning system (called LeSAC) based\u0000on argumentation theory to provide legal support to designers during the design\u0000process. Building on this, this paper explores how to provide designers with\u0000effective explanations for their legally relevant design decisions. We extend\u0000the previous system for providing explanations by specifying norms and the key\u0000legal or ethical principles for justifying actions in normative contexts.\u0000Considering that first-order logic has strong expressive power, in the current\u0000paper we adopt a first-order deontic logic system with deontic operators and\u0000preferences. We illustrate the advantages and necessity of introducing deontic\u0000logic and designing explanations under LeSAC by modelling two cases in the\u0000context of autonomous driving. In particular, this paper also discusses the\u0000requirements of the updated LeSAC to guarantee rationality, and proves that a\u0000well-defined LeSAC can satisfy the rationality postulate for rule-based\u0000argumentation frameworks. This ensures the system's ability to provide\u0000coherent, legally valid explanations for complex design decisions.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine Learning and Theory Ladenness -- A Phenomenological Account 机器学习与理论娴熟性 -- 一种现象学解释

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-17 DOI: arxiv-2409.11277

Alberto Termine, Emanuele Ratti, Alessandro Facchini

{"title":"Machine Learning and Theory Ladenness -- A Phenomenological Account","authors":"Alberto Termine, Emanuele Ratti, Alessandro Facchini","doi":"arxiv-2409.11277","DOIUrl":"https://doi.org/arxiv-2409.11277","url":null,"abstract":"In recent years, the dissemination of machine learning (ML) methodologies in\u0000scientific research has prompted discussions on theory ladenness. More\u0000specifically, the issue of theory ladenness has remerged as questions about\u0000whether and how ML models (MLMs) and ML modelling strategies are impacted by\u0000the domain theory of the scientific field in which ML is used and implemented\u0000(e.g., physics, chemistry, biology, etc). On the one hand, some have argued\u0000that there is no difference between traditional (pre ML) and ML assisted\u0000science. In both cases, theory plays an essential and unavoidable role in the\u0000analysis of phenomena and the construction and use of models. Others have\u0000argued instead that ML methodologies and models are theory independent and, in\u0000some cases, even theory free. In this article, we argue that both positions are\u0000overly simplistic and do not advance our understanding of the interplay between\u0000ML methods and domain theories. Specifically, we provide an analysis of theory\u0000ladenness in ML assisted science. Our analysis reveals that, while the\u0000construction of MLMs can be relatively independent of domain theory, the\u0000practical implementation and interpretation of these models within a given\u0000specific domain still relies on fundamental theoretical assumptions and\u0000background knowledge.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"209 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent 利用多代理思想树验证代理改进 LLM 推理

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-17 DOI: arxiv-2409.11527

Fatemeh Haji, Mazal Bethany, Maryam Tabar, Jason Chiang, Anthony Rios, Peyman Najafirad

{"title":"Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent","authors":"Fatemeh Haji, Mazal Bethany, Maryam Tabar, Jason Chiang, Anthony Rios, Peyman Najafirad","doi":"arxiv-2409.11527","DOIUrl":"https://doi.org/arxiv-2409.11527","url":null,"abstract":"Multi-agent strategies have emerged as a promising approach to enhance the\u0000reasoning abilities of Large Language Models (LLMs) by assigning specialized\u0000roles in the problem-solving process. Concurrently, Tree of Thoughts (ToT)\u0000methods have shown potential in improving reasoning for complex\u0000question-answering tasks by exploring diverse reasoning paths. A critical\u0000limitation in multi-agent reasoning is the 'Reasoner' agent's shallow\u0000exploration of reasoning paths. While ToT strategies could help mitigate this\u0000problem, they may generate flawed reasoning branches, which could harm the\u0000trustworthiness of the final answer. To leverage the strengths of both\u0000multi-agent reasoning and ToT strategies, we introduce a novel approach\u0000combining ToT-based Reasoner agents with a Thought Validator agent. Multiple\u0000Reasoner agents operate in parallel, employing ToT to explore diverse reasoning\u0000paths. The Thought Validator then scrutinizes these paths, considering a\u0000Reasoner's conclusion only if its reasoning is valid. This method enables a\u0000more robust voting strategy by discarding faulty reasoning paths, enhancing the\u0000system's ability to tackle tasks requiring systematic and trustworthy\u0000reasoning. Our method demonstrates superior performance compared to existing\u0000techniques when evaluated on the GSM8K dataset, outperforming the standard ToT\u0000strategy by an average 5.6% across four LLMs.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Navigating Process Mining: A Case study using pm4py 流程挖掘导航：使用 pm4py 的案例研究

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-17 DOI: arxiv-2409.11294

Ali Jlidi, László Kovács

{"title":"Navigating Process Mining: A Case study using pm4py","authors":"Ali Jlidi, László Kovács","doi":"arxiv-2409.11294","DOIUrl":"https://doi.org/arxiv-2409.11294","url":null,"abstract":"Process-mining techniques have emerged as powerful tools for analyzing event\u0000data to gain insights into business processes. In this paper, we present a\u0000comprehensive analysis of road traffic fine management processes using the\u0000pm4py library in Python. We start by importing an event log dataset and explore\u0000its characteristics, including the distribution of activities and process\u0000variants. Through filtering and statistical analysis, we uncover key patterns\u0000and variations in the process executions. Subsequently, we apply various\u0000process-mining algorithms, including the Alpha Miner, Inductive Miner, and\u0000Heuristic Miner, to discover process models from the event log data. We\u0000visualize the discovered models to understand the workflow structures and\u0000dependencies within the process. Additionally, we discuss the strengths and\u0000limitations of each mining approach in capturing the underlying process\u0000dynamics. Our findings shed light on the efficiency and effectiveness of road\u0000traffic fine management processes, providing valuable insights for process\u0000optimization and decision-making. This study demonstrates the utility of pm4py\u0000in facilitating process mining tasks and its potential for analyzing real-world\u0000business processes.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Networks for Vehicle Routing Problem 用于车辆路由问题的神经网络

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-17 DOI: arxiv-2409.11290

László Kovács, Ali Jlidi

引用次数: 0

ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework ReflectDiffu：通过 RL-Diffusion 框架在情感意向传染和模仿之间进行反射，以生成富有同情心的反应

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-16 DOI: arxiv-2409.10289

Jiahao Yuan, Zixiang Di, Zhiqing Cui, Guisong Yang, Usman Naseem

{"title":"ReflectDiffu: Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework","authors":"Jiahao Yuan, Zixiang Di, Zhiqing Cui, Guisong Yang, Usman Naseem","doi":"arxiv-2409.10289","DOIUrl":"https://doi.org/arxiv-2409.10289","url":null,"abstract":"Empathetic response generation necessitates the integration of emotional and\u0000intentional dynamics to foster meaningful interactions. Existing research\u0000either neglects the intricate interplay between emotion and intent, leading to\u0000suboptimal controllability of empathy, or resorts to large language models\u0000(LLMs), which incur significant computational overhead. In this paper, we\u0000introduce ReflectDiffu, a lightweight and comprehensive framework for\u0000empathetic response generation. This framework incorporates emotion contagion\u0000to augment emotional expressiveness and employs an emotion-reasoning mask to\u0000pinpoint critical emotional elements. Additionally, it integrates intent\u0000mimicry within reinforcement learning for refinement during diffusion. By\u0000harnessing an intent twice reflect the mechanism of\u0000Exploring-Sampling-Correcting, ReflectDiffu adeptly translates emotional\u0000decision-making into precise intent actions, thereby addressing empathetic\u0000response misalignments stemming from emotional misrecognition. Through\u0000reflection, the framework maps emotional states to intents, markedly enhancing\u0000both response empathy and flexibility. Comprehensive experiments reveal that\u0000ReflectDiffu outperforms existing models regarding relevance, controllability,\u0000and informativeness, achieving state-of-the-art results in both automatic and\u0000human evaluations.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers 通过因果变换器进行预测性自我监督的逻辑合成优化

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-16 DOI: arxiv-2409.10653

Raika Karimi, Faezeh Faez, Yingxue Zhang, Xing Li, Lei Chen, Mingxuan Yuan, Mahdi Biparva

{"title":"Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers","authors":"Raika Karimi, Faezeh Faez, Yingxue Zhang, Xing Li, Lei Chen, Mingxuan Yuan, Mahdi Biparva","doi":"arxiv-2409.10653","DOIUrl":"https://doi.org/arxiv-2409.10653","url":null,"abstract":"Contemporary hardware design benefits from the abstraction provided by\u0000high-level logic gates, streamlining the implementation of logic circuits.\u0000Logic Synthesis Optimization (LSO) operates at one level of abstraction within\u0000the Electronic Design Automation (EDA) workflow, targeting improvements in\u0000logic circuits with respect to performance metrics such as size and speed in\u0000the final layout. Recent trends in the field show a growing interest in\u0000leveraging Machine Learning (ML) for EDA, notably through ML-guided logic\u0000synthesis utilizing policy-based Reinforcement Learning (RL) methods.Despite\u0000these advancements, existing models face challenges such as overfitting and\u0000limited generalization, attributed to constrained public circuits and the\u0000expressiveness limitations of graph encoders. To address these hurdles, and\u0000tackle data scarcity issues, we introduce LSOformer, a novel approach\u0000harnessing Autoregressive transformer models and predictive SSL to predict the\u0000trajectory of Quality of Results (QoR). LSOformer integrates cross-attention\u0000modules to merge insights from circuit graphs and optimization sequences,\u0000thereby enhancing prediction accuracy for QoR metrics. Experimental studies\u0000validate the effectiveness of LSOformer, showcasing its superior performance\u0000over baseline architectures in QoR prediction tasks, where it achieves\u0000improvements of 5.74%, 4.35%, and 17.06% on the EPFL, OABCD, and proprietary\u0000circuits datasets, respectively, in inductive setup.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0