Profit Sharing の不完全知覚環境下への拡張 : PS-r^*の提案と評価

概要

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We know the rationality theorem of Profit Sharing(PS) [Miyazaki 94, Miyazaki 99b] and the Rational Policy Making algorithm(RPM) [Miyazaki 99a] to guarantee the rationality in a typical class of Partially Observable Markov Decision Processes (POMDPs). In this paper, we focus on the whole class of POMDPs and propose PS-r that is an algorithm connected PS and RPM with random selection. In the first, we have analyzed the behavior of PS-r. We have derived that the maximum value of the step to get a reward by PS-r divided by that of random selection is $({\Large r\frac{(1+\frac{M-1}{r})^n}{M^n}})$ where $n$</pre> is the maximum number of state that senses same state due to the agents sensory limitation and M is the number of actions. Furthermore, we propose PS-r* that can improve the behavior of PS-r. Through numerical examples, we conform the effectiveness of PS-r*.
社団法人　人工知能学会の論文
2003-11-01