文章基本信息

标题：Cognitive Distance Learning Problem Solver Reduces Search Cost through Learning Processes
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作者：Hiroshi Yamakawa ; Yuji Miyamoto ; Takayuki Baba 等
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：2002
卷号：17
期号：1
页码：1-13
DOI：10.1527/tjsai.17.1
出版社：The Japanese Society for Artificial Intelligence
摘要：Our proposed cognitive distance learning problem solver generates sequence of actions from initial state to goal states in problem state space. This problem solver learns cognitive distance (path cost) of arbitrary combination of two states. Action generation at each state is selection of next state that has minimum cognitive distance to the goal, like Q-learning agent. In this paper, first, we show that our proposed method reduces search cost than conventional search method by analytical simulation in spherical state space. Second, we show that an average search cost is more reduced more the prior learning term is long and our problem solver is familiar to the environment, by a computer simulation in a tile world state space. Third, we showed that proposed problem solver is superior to the reinforcement learning techniques when goal is changed by a computer simulation. Forth, we found that our simulation result consist with psychological experimental results.
关键词：general problem solver ; search ; reinforcement learning ; planner ; path cost