著者

上野 敦志 中須賀 真一 堀 浩一

出版者

社団法人人工知能学会

雑誌

人工知能学会誌 (ISSN:09128085)

巻号頁・発行日

vol.15, no.2, pp.297-308, 2000-03-01

被引用文献数

9

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Real robots should be able to adapt autonomously to various environments in order to go on executing their task without a break. For this purpose, they should be able to learn how to abstract useful information from a huge amount of information in the environment while executing their task. This paper proposes a new architecture which performs categorical learning and behavioral learning parallelly with task execution. We call the architecture Situation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture can learn efficiently and adapt to unexpected changes of the environment autonomously.