arXiv - CS - Artificial Intelligence最新文献_第7页

An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting 在多标准排序中学习潜在非单调偏好的基于偏好激发的增量方法

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-04 DOI: arxiv-2409.02760

Zhuolin Li, Zhen Zhang, Witold Pedrycz

{"title":"An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting","authors":"Zhuolin Li, Zhen Zhang, Witold Pedrycz","doi":"arxiv-2409.02760","DOIUrl":"https://doi.org/arxiv-2409.02760","url":null,"abstract":"This paper introduces a novel incremental preference elicitation-based\u0000approach to learning potentially non-monotonic preferences in multi-criteria\u0000sorting (MCS) problems, enabling decision makers to progressively provide\u0000assignment example preference information. Specifically, we first construct a\u0000max-margin optimization-based model to model potentially non-monotonic\u0000preferences and inconsistent assignment example preference information in each\u0000iteration of the incremental preference elicitation process. Using the optimal\u0000objective function value of the max-margin optimization-based model, we devise\u0000information amount measurement methods and question selection strategies to\u0000pinpoint the most informative alternative in each iteration within the\u0000framework of uncertainty sampling in active learning. Once the termination\u0000criterion is satisfied, the sorting result for non-reference alternatives can\u0000be determined through the use of two optimization models, i.e., the max-margin\u0000optimization-based model and the complexity controlling optimization model.\u0000Subsequently, two incremental preference elicitation-based algorithms are\u0000developed to learn potentially non-monotonic preferences, considering different\u0000termination criteria. Ultimately, we apply the proposed approach to a credit\u0000rating problem to elucidate the detailed implementation steps, and perform\u0000computational experiments on both artificial and real-world data sets to\u0000compare the proposed question selection strategies with several benchmark\u0000strategies.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193865","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

Intensional FOL: Many-Sorted Extension 多维 FOL：多分类扩展

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-03 DOI: arxiv-2409.04469

Zoran Majkic

引用次数: 0

A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial 在口腔健康临床试验中部署在线强化学习算法

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-03 DOI: arxiv-2409.02069

Anna L. Trella, Kelly W. Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy

{"title":"A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial","authors":"Anna L. Trella, Kelly W. Zhang, Hinal Jajal, Inbal Nahum-Shani, Vivek Shetty, Finale Doshi-Velez, Susan A. Murphy","doi":"arxiv-2409.02069","DOIUrl":"https://doi.org/arxiv-2409.02069","url":null,"abstract":"Dental disease is a prevalent chronic condition associated with substantial\u0000financial burden, personal suffering, and increased risk of systemic diseases.\u0000Despite widespread recommendations for twice-daily tooth brushing, adherence to\u0000recommended oral self-care behaviors remains sub-optimal due to factors such as\u0000forgetfulness and disengagement. To address this, we developed Oralytics, a\u0000mHealth intervention system designed to complement clinician-delivered\u0000preventative care for marginalized individuals at risk for dental disease.\u0000Oralytics incorporates an online reinforcement learning algorithm to determine\u0000optimal times to deliver intervention prompts that encourage oral self-care\u0000behaviors. We have deployed Oralytics in a registered clinical trial. The\u0000deployment required careful design to manage challenges specific to the\u0000clinical trials setting in the U.S. In this paper, we (1) highlight key design\u0000decisions of the RL algorithm that address these challenges and (2) conduct a\u0000re-sampling analysis to evaluate algorithm design decisions. A second phase\u0000(randomized control trial) of Oralytics is planned to start in spring 2025.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"156 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193868","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

Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework 为带返工的动态技术人员路由学习与状态相关的策略参数化

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-03 DOI: arxiv-2409.01815

Jonas Stein, Florentin D Hildebrandt, Barrett W Thomas, Marlin W Ulmer

{"title":"Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework","authors":"Jonas Stein, Florentin D Hildebrandt, Barrett W Thomas, Marlin W Ulmer","doi":"arxiv-2409.01815","DOIUrl":"https://doi.org/arxiv-2409.01815","url":null,"abstract":"Home repair and installation services require technicians to visit customers\u0000and resolve tasks of different complexity. Technicians often have heterogeneous\u0000skills and working experiences. The geographical spread of customers makes\u0000achieving only perfect matches between technician skills and task requirements\u0000impractical. Additionally, technicians are regularly absent due to sickness.\u0000With non-perfect assignments regarding task requirement and technician skill,\u0000some tasks may remain unresolved and require a revisit and rework. Companies\u0000seek to minimize customer inconvenience due to delay. We model the problem as a\u0000sequential decision process where, over a number of service days, customers\u0000request service while heterogeneously skilled technicians are routed to serve\u0000customers in the system. Each day, our policy iteratively builds tours by\u0000adding \"important\" customers. The importance bases on analytical considerations\u0000and is measured by respecting routing efficiency, urgency of service, and risk\u0000of rework in an integrated fashion. We propose a state-dependent balance of\u0000these factors via reinforcement learning. A comprehensive study shows that\u0000taking a few non-perfect assignments can be quite beneficial for the overall\u0000service quality. We further demonstrate the value provided by a state-dependent\u0000parametrization.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"248 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193900","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

Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria 基于词典优化的方法，学习具有非单调标准的多标准排序代表模型

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-03 DOI: arxiv-2409.01612

Zhen Zhang, Zhuolin Li, Wenyu Yu

{"title":"Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria","authors":"Zhen Zhang, Zhuolin Li, Wenyu Yu","doi":"arxiv-2409.01612","DOIUrl":"https://doi.org/arxiv-2409.01612","url":null,"abstract":"Deriving a representative model using value function-based methods from the\u0000perspective of preference disaggregation has emerged as a prominent and growing\u0000topic in multi-criteria sorting (MCS) problems. A noteworthy observation is\u0000that many existing approaches to learning a representative model for MCS\u0000problems traditionally assume the monotonicity of criteria, which may not\u0000always align with the complexities found in real-world MCS scenarios.\u0000Consequently, this paper proposes some approaches to learning a representative\u0000model for MCS problems with non-monotonic criteria through the integration of\u0000the threshold-based value-driven sorting procedure. To do so, we first define\u0000some transformation functions to map the marginal values and category\u0000thresholds into a UTA-like functional space. Subsequently, we construct\u0000constraint sets to model non-monotonic criteria in MCS problems and develop\u0000optimization models to check and rectify the inconsistency of the decision\u0000maker's assignment example preference information. By simultaneously\u0000considering the complexity and discriminative power of the models, two distinct\u0000lexicographic optimization-based approaches are developed to derive a\u0000representative model for MCS problems with non-monotonic criteria. Eventually,\u0000we offer an illustrative example and conduct comprehensive simulation\u0000experiments to elaborate the feasibility and validity of the proposed\u0000approaches.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"156 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193903","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

LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning LASP：大型语言模型辅助人工智能规划技术现状调查

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-03 DOI: arxiv-2409.01806

Haoming Li, Zhaoliang Chen, Jonathan Zhang, Fei Liu

引用次数: 0

Here's Charlie! Realising the Semantic Web vision of Agents in the age of LLMs 查理来了在法律硕士时代实现代理的语义网愿景

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-03 DOI: arxiv-2409.04465

Jesse Wright

{"title":"Here's Charlie! Realising the Semantic Web vision of Agents in the age of LLMs","authors":"Jesse Wright","doi":"arxiv-2409.04465","DOIUrl":"https://doi.org/arxiv-2409.04465","url":null,"abstract":"This paper presents our research towards a near-term future in which legal\u0000entities, such as individuals and organisations can entrust semi-autonomous\u0000AI-driven agents to carry out online interactions on their behalf. The author's\u0000research concerns the development of semi-autonomous Web agents, which consult\u0000users if and only if the system does not have sufficient context or confidence\u0000to proceed working autonomously. This creates a user-agent dialogue that allows\u0000the user to teach the agent about the information sources they trust, their\u0000data-sharing preferences, and their decision-making preferences. Ultimately,\u0000this enables the user to maximise control over their data and decisions while\u0000retaining the convenience of using agents, including those driven by LLMs. In view of developing near-term solutions, the research seeks to answer the\u0000question: \"How do we build a trustworthy and reliable network of\u0000semi-autonomous agents which represent individuals and organisations on the\u0000Web?\". After identifying key requirements, the paper presents a demo for a\u0000sample use case of a generic personal assistant. This is implemented using\u0000(Notation3) rules to enforce safety guarantees around belief, data sharing and\u0000data usage and LLMs to allow natural language interaction with users and\u0000serendipitous dialogues between software agents.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194039","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

H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark H-ARC：对人类在抽象与推理语料库基准上的表现的可靠评估

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-02 DOI: arxiv-2409.01374

Solim LeGris, Wai Keen Vong, Brenden M. Lake, Todd M. Gureckis

{"title":"H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark","authors":"Solim LeGris, Wai Keen Vong, Brenden M. Lake, Todd M. Gureckis","doi":"arxiv-2409.01374","DOIUrl":"https://doi.org/arxiv-2409.01374","url":null,"abstract":"The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis\u0000benchmark designed to test challenging out-of-distribution generalization in\u0000humans and machines. Since 2019, limited progress has been observed on the\u0000challenge using existing artificial intelligence methods. Comparing human and\u0000machine performance is important for the validity of the benchmark. While\u0000previous work explored how well humans can solve tasks from the ARC benchmark,\u0000they either did so using only a subset of tasks from the original dataset, or\u0000from variants of ARC, and therefore only provided a tentative estimate of human\u0000performance. In this work, we obtain a more robust estimate of human\u0000performance by evaluating 1729 humans on the full set of 400 training and 400\u0000evaluation tasks from the original ARC problem set. We estimate that average\u0000human performance lies between 73.3% and 77.2% correct with a reported\u0000empirical average of 76.2% on the training set, and between 55.9% and 68.9%\u0000correct with a reported empirical average of 64.2% on the public evaluation\u0000set. However, we also find that 790 out of the 800 tasks were solvable by at\u0000least one person in three attempts, suggesting that the vast majority of the\u0000publicly available ARC tasks are in principle solvable by typical crowd-workers\u0000recruited over the internet. Notably, while these numbers are slightly lower\u0000than earlier estimates, human performance still greatly exceeds current\u0000state-of-the-art approaches for solving ARC. To facilitate research on ARC, we\u0000publicly release our dataset, called H-ARC (human-ARC), which includes all of\u0000the submissions and action traces from human participants.","PeriodicalId":501479,"journal":{"name":"arXiv - CS - Artificial Intelligence","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193872","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

Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving 整合端到端和模块化驾驶方法，实现自动驾驶中的在线拐角检测

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-02 DOI: arxiv-2409.01178

Gemb Kaljavesi, Xiyan Su, Frank Diermeyer

引用次数: 0

Unlocking the Wisdom of Large Language Models: An Introduction to The Path to Artificial General Intelligence 开启大型语言模型的智慧：人工通用智能之路导论

arXiv - CS - Artificial Intelligence Pub Date : 2024-09-02 DOI: arxiv-2409.01007

Edward Y. Chang

引用次数: 0