作者: Anthony R. Cassandra
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摘要: An increasing number of researchers in many areas are becoming interested the application partially observable Markov decision process (pomdp) model to problems with hidden state. This can account for both state transition and observation uncertainty. The majority recent research interest pomdp has been artificial intelligence community as such, applied a limited range domains. main purpose this paper is show wider applicability by way surveying potential pomdps.