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文章基本信息

  • 标题:Modeling Reinforcement Learning using an Extended BDI logic TOMATOes
  • 本地全文:下载
  • 作者:Shiro TAKATA ; Naoyuki NIDE ; Megumi FUJITA
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2011
  • 卷号:26
  • 期号:1
  • 页码:156-165
  • DOI:10.1527/tjsai.26.156
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要:TOMATOes is an extension of BDI logic, which introduced probabilistic state transitions and fix-point operators. Using TOMATOes, we can strictly describe and infer various properties of rational agents with those extended notions. In this paper, we give a detailed explanation of modeling of reinforcement learning with the Kripke structure used in TOMATOes, called BDI structure, and the description of transaction graph with policy using TOMATOes. In addition, we give some issues on rational agents for practical reasoning with the description of transaction graph using TOMATOes.
  • 关键词:BDI logic ; reinforcement learning
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