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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Solving Planning Under Uncertainty: Quantitative and Qualitative Approach
verfasst von : Minghao Yin, Jianan Wang, Wenxiang Gu
Erschienen in: Theoretical Advances and Applications of Fuzzy Logic and Soft Computing
Verlag: Springer Berlin Heidelberg
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Classical decision-theoretic planning methods assume that the probabilistic model of the domain is always accurate. We present two algorithms rLAO* and qLAO* in this paper. rLAO* and qLAO* can solve uncertainty Markov decision problems and qualitative Markov decision problems respectively. We prove that given an admissible heuristic function, both rLAO* and qLAO* can find an optimal solution. Experimental results also show that rLAO* and qLAO* inherit the merits of excellent performance of LAO* for solving uncertainty problems.