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检索条件"任意字段=6th International Workshop on Programming Multi-Agent Systems"

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multi-agent Scheduling for Shared Manufacturing systems 6

Multi-agent Scheduling for Shared Manufacturing Systems

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6th international Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. this research focuses on multi-agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. the study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 the Authors.

关键词： Mixed-integer linear programming

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OVERLAY 2024 - Short Paper Proceedings of the 6th international workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis

OVERLAY 2024 - Short Paper Proceedings of the 6th Internatio...

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6th international workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis, OVERLAY 2024

the proceedings contain 16 papers. the topics discussed include: model checking of optimal LTL and ASAP properties;on optimizing simulation-based verification of cyber-physical systems via statistical model checking: a preliminary work;recent results on computable and compositional semantics for hybrid systems;temporal many-valued conditional logics: an abridged report;towards ASP-based minimal unsatisfiable cores enumeration for LTLf;formalizing decisional and operational roles in legal contracts via term-modal logic;integrating L0 regularization into multi-layer logical perceptron for interpretable classification;and automated synthesis of certified neural networks: initial results and open research lines.

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Knowledge Level Support for programming agents to Interact in Regulated Online Forums 28th

Knowledge Level Support for Programming Agents to Interact...

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28th international workshop on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of multi-agent systems, COINE 2024

作者： Hafiene, Nesrine Nardin, Luis G. Boissier, Olivier Mines Saint-Etienne Univ Clermont Auvergne INP Clermont Auvergne CNRS UMR 6158 LIMOS Saint-Etienne42023 France

ISBN: (纸本)9783031820380

the Web is populated by autonomous agents that act on behalf of users to perform tasks on the Web. these agents are required to interact with online systems to achieve their goals. However, they are usually designed to interact with specific online systems, preventing them from interacting with those whose structure, interaction methods, and regulations are a priori unknown. We propose a means to overcome this limitation by representing the online systems’ resources and regulations that agents can use to make decisions and act. Our approach can be split into two parts. First, we introduce a knowledge layer structured along with the multi-agent Oriented programming approach to describe Web resources and their regulations using ontology. Second, we introduce an agent that uses this knowledge layer for reasoning and acting in compliance with the regulations towards achieving its goals. We use the online forum as a running example to highlight the applicability and usefulness of our approach. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge representation

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An agent-Centric Perspective on Norm Enforcement and Sanctions 28th

An Agent-Centric Perspective on Norm Enforcement and Sanct...

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28th international workshop on Coordination, Organizations, Institutions, Norms, and Ethics for Governance of multi-agent systems, COINE 2024

作者： Yan, Elena Nardin, Luis G. Hübner, Jomi F. Boissier, Olivier Mines Saint-Etienne Univ Clermont Auvergne INP Clermont Auvergne CNRS UMR 6158 LIMOS Saint-Etienne42023 France Department of Automation and Systems Engineering Federal University of Santa Catarina Florianópolis Brazil

ISBN: (纸本)9783031820380

In increasingly autonomous and highly distributed multi-agent systems, centralized coordination becomes impractical and raises the need for governance and enforcement mechanisms from an agent-centric perspective. In our conceptual view, sanctioning norm enforcement is part of this agent-centric perspective and they aim at promoting norm compliance while preserving agents’ autonomy. the few works dealing with sanctioning norm enforcement and sanctions from the agent-centric perspective present limitations regarding the representation of sanctions and the comprehensiveness of their norm enforcement process. To address these drawbacks, we propose the NPL(s), an extension of the NPL normative programming language enriched with the representation of norms and sanctions as first-class abstractions. We also propose a BDI normative agent architecture embedding an engine for processing the NPL(s) language and a set of capabilities for approaching more comprehensively the sanctioning norm enforcement process. We apply our contributions in a case study for improving the robustness of agents’ decision-making in a production automation system. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Norm Enforcement Normative agent Architecture Normative programming Language Sanctions

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Assessing Cognitive Load in programming Exercises Based on Readability and Lexical Richness 17th

Assessing Cognitive Load in Programming Exercises Based on...

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17th workshop of Hybrid Intelligent systems, HIS 2024, 17th workshop on Intelligent Learning Environments, WILE 2024 and 6th workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international Conference on Artificial Intelligence, MICAI 2024

作者： García-Gorrostieta, Jesús Miguel González-López, Samuel López-López, Aurelio Ponce-Mendoza, Ulises Madrid-Monteverde, José David Universidad de la Sierra Sonora Moctezuma Mexico Technological Institute of Nogales Sonora Mexico Instituto Nacional de Astrofísica Óptica y Electrónica Puebla Tonantzintla Mexico

ISBN: (纸本)9783031838811

In this paper, we explore the impact of problem statement readability and lexical richness on cognitive load during programming exercises. Cognitive load theory suggests that the complexity of instructions can significantly affect student performance, especially in tasks requiring high mental effort. Using NASA-TLX, this study measures students’ cognitive load working on JavaScript and React programming problems with varying readability levels. Results show that students with higher programming proficiency tend to experience lower cognitive load, even with more complex problem descriptions, while less experienced students benefit from simplified instructions. this paper provides recommendations for instructional design to optimize cognitive load based on student proficiency, that could be incorporated in a learning environment. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： NASA

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Joint workshop of the 9th Financial Technology and Natural Language Processing, FinNLP 2025, the 6th Financial Narrative Processing, FNP 2025, and the 1st workshop on Large Language Models for Finance and Legal, LLMFinLegal 2025

Joint Workshop of the 9th Financial Technology and Natural L...

引用

Joint workshop of the 9th Financial Technology and Natural Language Processing, FinNLP 2025, the 6th Financial Narrative Processing, FNP 2025, and the 1st workshop on Large Language Models for Finance and Legal, LLMFinLegal 2025, co-located with the 31st international Conference on Computational Linguistics, COLING 2025

ISBN: (纸本)9798891762091

the proceedings contain 48 papers. the topics discussed include: Chat Bankman-Fried: an exploration of LLM alignment in finance;GraphRAG analysis for financial narrative summarization and a framework for optimizing domain adaptation;BuDDIE: a business document dataset for multi-task information extraction;FinMoE: a MoE-based large Chinese financial language model;bridging the gap: efficient cross-lingual NER in low-resource financial domain;evaluating financial literacy of large language models through domain specific languages for plain text accounting;synthetic data generation using large language models for financial question answering;training LayoutLM from scratch for efficient named-entity recognition in the insurance domain;and investigating the effectiveness of length based rewards in DPO for building conversational financial question answering systems.

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17th workshop of Hybrid Intelligent systems, HIS 2024, 17th workshop on Intelligent Learning Environments, WILE 2024 and 6th workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international Conference on Artificial Intelligence, MICAI 2024

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

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ISBN: (纸本)9783031838781

the proceedings contain 38 papers. the special focus in this conference is on Hybrid Intelligent systems. the topics include: Integration of Alexa and Social IoT for Generation X: A Study on the Optimization Use of Smart Treadmill to Improve Physical Health;logarithmic Weighted Random Selector Algorithm: A Novel Approach for Biasing Selection Based on Positional Order Without Hyperparameters;design and Implementation of a Machine Learning Model for Soccer Match Prediction Based on Player Statistics;electric Scooters and Renewable Energy Integration Associated with Tourist Parks: A Dijkstra-Based Model for Smart Mobility Optimization;use of Convolutional Neural Networks for the Recognition of Bird Species in Risk Categories in the State of Chihuahua;exploring Deep Learning Applications in Neurodegenerative Diseases: A State-of-the-Art Review;Automated Insights: LLMs in Neurodegenerative Disease Research and Comparison;convolutional Neural Network Models for Classifying of Peach (Prunus persica L);Cleaning Binary Distortion on MNIST Dataset;rule-Based Expert System with Bayesian theory and Fuzzy Inference for Vocational Guidance: A Tool to Prevent School Dropouts;detection of Basic Motorcycle Faults Using a Fuzzy Bayesian Expert System;intervention Model with Data Mining Techniques to Work with Dating Violence Victims Using a Social Support Network;enhanced Pest Detection Using Quaternion-Based Image Segmentation in Yellow Sticky Trap Samples for Precision Agriculture;Optimization of a Treatment Plant through the Incorporation of New Waste Separation Components: A TOPSIS-Focused multi-criteria Analysis Approach;multicriteria Analysis Applied to the Selection of Shopping Centers for a Family-Owned Restaurant Business in Smart Cities: A Case Study in Ciudad Juárez.

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

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ISBN: (纸本)9783031838811

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Experimental Study for Automatic Feature Construction to Segment Images of Lungs Affected by COVID-19 Using Genetic programming 17th

Experimental Study for Automatic Feature Construction to S...

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作者： Herrera-Sánchez, David Fuentes-Tomás, José-Antonio Acosta-Mesa, Héctor-Gabriel Mezura-Montes, Efrén Artificial Intelligence Research Institute University of Veracruz Veracruz Xalapa91097 Mexico

ISBN: (纸本)9783031838811

Image segmentation is a challenging task due to image variations, such as illumination, background, noise, and others. there are several segmentation methods, but the requirement of prior knowledge and parameter setting makes it hard to perform a good segmentation, especially in medical images where an expert is needed to make the segmentation accordingly with the prior knowledge to determine the area of interest. In this work, we are focused on the feature construction to make an image segmentation of computerized tomography scans of lungs affected by COVID-19. Genetic programming (GP) is used to evolve a program to extract and construct features from the image to make a segmentation where the target is to find the affected area. the flexibility that offers GP allows us to face the segmentation task and know which functions are used in the final program, leading to an interpretable solution. the results of the experiments demonstrate that GP is capable of extracting and constructing features from the Computerized Tomography images to perform the segmentation of lungs affected by COVID-19, achieving values of 0.59 of F1-score metric to measure the segmentation performance. Furthermore, the experimental results determine the appropriate parameters for the evolutionary process. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Investigating the effectiveness of length based rewards in DPO for building Conversational Financial Question Answering systems 9

Investigating the effectiveness of length based rewards in D...

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作者： Yadav, Anushka Rallabandi, Sai Krishna Dakle, Parag Pravin Raghavan, Preethi University of Massachusetts Amherst United States Fidelity Investments United States

ISBN: (纸本)9798891762091

In this paper, we address the numerical reasoning challenges of financial question-answering systems. We propose a two-stage approach where models first generate intermediate calculations and then produce the final answer. We perform two experiments to evaluate the performance of our approach. In the first, we compare single-step and multi-step approaches, demonstrating that incorporating intermediate calculations significantly improves numerical accuracy. In the second experiment, we compare traditional DPO and iterative DPO (iDPO) with length-regularized DPO. We show that while traditional DPO reduced parsing errors, it introduces verbosity;iDPO improves reasoning iteratively but faces diminishing returns. On the other hand, Length-regularized DPO reduces verbosity of intermediate calculation as well as enhances numerical accuracy across all models. these results highlight the potential of combining intermediate reasoning steps with domain-specific optimizations to build robust financial question-answering systems. © 2025 Association for Computational Linguistics.

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