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Titlebook: Advanced Topics in Artificial Intelligence; 11th Australian Join Grigoris Antoniou,John Slaney Conference proceedings 1998 Springer-Verlag

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发表于 2025-3-21 18:30:46 | 显示全部楼层 |阅读模式
期刊全称Advanced Topics in Artificial Intelligence
期刊简称11th Australian Join
影响因子2023Grigoris Antoniou,John Slaney
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advanced Topics in Artificial Intelligence; 11th Australian Join Grigoris Antoniou,John Slaney Conference proceedings 1998 Springer-Verlag
影响因子This book presents the thoroughly refereed post-conference proceedings of the 11th Australian Joint Conference on Artificial Intelligence, AI‘98, held in Brisbane, Australia in July 1998. .The 28 revised full papers presented in the book were carefully reviewed and selected from twice as many papers accepted for presentation at the conference. .Among the topics covered are philosophical issues, fuzzy logic, agent systems, AI logics, planning, knowledge representation, automated deduction, intelligent agents, learning, constraint solving, and neural networks.
Pindex Conference proceedings 1998
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发表于 2025-3-21 23:00:09 | 显示全部楼层
The normal lymph node and spleen,ndamental problem are discussed, including the proliferation of correspondences; the need to posit an observer; the inability to account for error from the system’s point of view; and the radical incompatibility between representation by correspondence and evolutionary or developmental accounts of knowledge.
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https://doi.org/10.1007/978-3-540-39606-2a consequence, 3APL has at least the same expressive power as AgentSpeak(L). The comparison yields some new insights into the features of the agent languages. One of the results in that AgentSpeak(L) can be substantially simplified.
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0302-9743 I‘98, held in Brisbane, Australia in July 1998. .The 28 revised full papers presented in the book were carefully reviewed and selected from twice as many papers accepted for presentation at the conference. .Among the topics covered are philosophical issues, fuzzy logic, agent systems, AI logics, pla
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https://doi.org/10.1007/978-3-540-39606-2chieved by creating a hybrid system which uses fuzzy logic techniques combined with certainty factors, and a fixed point defuzzification technique. This allows the system to reason under uncertainty and choose the most appropriate direction and speed from a defined list.
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https://doi.org/10.1007/978-3-540-39606-2rning. The dynamic self-organizing map is used to partition the space of sequences of situations into different regions. Reinforcement learning refines the content of the regions based on the experience of the agent. We show the effectiveness of the method and evaluate it through several simulation studies.
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https://doi.org/10.1007/978-3-540-39606-2t can draw. Using it, we show that defeaters do not add to the expressiveness of defeasible logic, among other results. The analysis also supports the restriction of defeasible logic to admit only acyclic superiority relations.
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