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Titlebook: Agents and Artificial Intelligence; 15th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2024 The Edit

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發(fā)表于 2025-3-21 16:55:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Agents and Artificial Intelligence
期刊簡(jiǎn)稱15th International C
影響因子2023Ana Paula Rocha,Luc Steels,Jaap van den Herik
視頻videohttp://file.papertrans.cn/152/151227/151227.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Agents and Artificial Intelligence; 15th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2024 The Edit
影響因子.This book contains the revised and extended versions of selected papers from the?15th International Conference on Agents and Artificial Intelligence, ICAART 2023, held in?Lisbon, Portugal, during February 22–24, 2023...The 23 full papers included in this book were carefully reviewed and selected from 306 submissions. The conference was organized in 2 tracks as follows: .One track focuses on Agents, Multi-Agent Systems and Software Platforms, Distributed Problem Solving and Distributed AI in general. The other track focuses mainly on Artificial Intelligence, Knowledge Representation, Planning, Learning, Scheduling, Perception Reactive AI Systems, and Evolutionary Computing and other topics related to Intelligent Systems and Computational Intelligence..
Pindex Conference proceedings 2024
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Estimating the?Spread of?COVID-19 Due to?Transportation Networks Using Agent-Based Modelingand devising transportation plans. As infections continue to surge exponentially, the need for carefully regulating travel has become paramount. However, existing research has struggled to address this issue comprehensively for India, a country characterized by diverse transportation networks and a
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Dynamic Communities: A Novel Recommendation Approach for?Individuals and?Groupsis paper proposes a novel dynamic and overlapping community-based recommender system framework, which considers the evolving nature of users’ preferences to suggest items distinctly for both individuals and groups. Using temporal networks to capture the dynamic interplay of users’ interests, we appl
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Towards Improving Multivariate Time-Series Forecasting Using Weighted Linear Stackinga has sparked interest, with researchers and practitioners developing approaches and models in an attempt to accurately forecast such type of data. Amongst the three main forecasting approaches, that is, the Separate Model Forecasting Approach (SMFA), the Global Model Forecasting Approach (GMFA) and
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