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Titlebook: Autonomous Intelligent Systems: Multi-Agents and Data Mining; Second International Vladimir Gorodetsky,Chengqi Zhang,Longbing Cao Conferenc

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發(fā)表于 2025-3-21 19:51:59 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Autonomous Intelligent Systems: Multi-Agents and Data Mining
期刊簡稱Second International
影響因子2023Vladimir Gorodetsky,Chengqi Zhang,Longbing Cao
視頻videohttp://file.papertrans.cn/167/166779/166779.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Autonomous Intelligent Systems: Multi-Agents and Data Mining; Second International Vladimir Gorodetsky,Chengqi Zhang,Longbing Cao Conferenc
影響因子Since early 1990, multi-agent systems (MAS), data mining, and knowledge d- covery (KDD) have remained areas of high interest in the research and - velopment of intelligent information technologies. Indeed, MAS o?ers powerful metaphors for information system conceptualization, a range of new techniques, and technologies speci?cally focused on the design and implementation of lar- scale open distributed intelligent systems. KDD also provides intelligent inf- mation technology with powerful ideas, algorithms, and software means to help cope with the main problem of arti?cial intelligence, formulated in the we- known question “Where does the knowledge come from?”, thus actually making modern applications intelligent and adaptive. The evident recent trend in both science and industry is to integrate and take advantage of both technologies. The existing experience with combined application of multi-agent technology to design architectures of distributed (- erarchical and peer-to-peer) data mining and KDD systems, as well as the u- lization of data mining and KDD achievements to provide enhanced intelligence of MAS, con?rms the fact that both technologies are capable of mutual enri- ment
Pindex Conference proceedings 2007
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Hans-J?rg Kreowski,Grzegorz Rozenberge number of bids grow. This paper shows how to build an ant system for autonomous agents. In addition, it assigns customer agents’ services to supplier agents’ bids in the best possible way while considering several factors.
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Géza Kulcsár,Malte Lochau,Andy Schürrt the sub-datasets for the classifier training agents are obtained by dividing the features rather than by dividing the sample set in distribution environment. Experimental results show that this method has a preferable performance on high dimensional datasets.
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Graph Rewriting and Relabeling with PBPO solutions of two top Botvinnik’s tests – the Reti and Nodareishvili etudes. For min max game tree based search algorithms these etudes appears to be computationally hard due the depth of the required analysis and very dependence on the expert knowledge.
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On Competing Agents Consistent with Expert Knowledge solutions of two top Botvinnik’s tests – the Reti and Nodareishvili etudes. For min max game tree based search algorithms these etudes appears to be computationally hard due the depth of the required analysis and very dependence on the expert knowledge.
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