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Titlebook: Agents and Artificial Intelligence; 14th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2022 The Edit

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发表于 2025-3-21 16:29:35 | 显示全部楼层 |阅读模式
期刊全称Agents and Artificial Intelligence
期刊简称14th International C
影响因子2023Ana Paula Rocha,Luc Steels,Jaap van den Herik
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Agents and Artificial Intelligence; 14th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2022 The Edit
影响因子This book contains the revised and extended versions of selected papers from the 14th International Conference on Agents and Artificial Intelligence, ICAART 2022, which took place virtually during February 3–5, 2022. The conference was originally planned to take place in Vienna, Austria, but had to change to an online format due to the COVID-19 pandemic..The 9 full papers included in this book were carefully reviewed and selected from 302 submissions. They were organized in topical sections as follows: agents; artificial intelligence..
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Negotiation Protocol with Learned Handover of Important Tasks for Planned Suspensions in Multi-agent this information by transferring the important tasks to others in advance. The proposed novel negotiation method between agents is designed for this purpose based on the existing method for MACPP and can reduce the performance degradation caused by planned suspensions. A comparison with the convent
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Comparing Beta-VAE to WGAN-GP for Time Series Augmentation to Improve Classification Performancein classification accuracy of +0,722% when used in the 1-nearest neighbor algorithm, and +1,856% when used for training the MiniRocket. The use of WGAN-GP generated synthetic sets to train classifiers led to average increases in classification accuracy of +0,654% with the 1-nearest neighbor algorith
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Generating the Gopher’s Grounds: Form, Function, Order, and Alignmenter those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when fa
发表于 2025-3-22 15:17:33 | 显示全部楼层
Domain Dependent Parameter Setting in SAT Solver Using Machine Learning Techniquesine learning approach that predicts the parameters of heuristic from the underlying structure of a graph derived from the input SAT instance. Using this approach we effectively fine tune the SAT solver for specific problem domain.
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Culture History and Convergent Evolution this information by transferring the important tasks to others in advance. The proposed novel negotiation method between agents is designed for this purpose based on the existing method for MACPP and can reduce the performance degradation caused by planned suspensions. A comparison with the convent
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Culture Paves The New Silk Roadss crucial. Multi-agent reinforcement learning methods are an apt choice for traffic signal control of large scale road networks as they can learn to perform predictive control actions that will reduce overall network congestion dynamically at scale. In this paper, we extend the work done in [.] to t
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