文章基本信息

标题：Planning in Markov Stochastic Task Domains
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作者：Mr. Yong Lin ; Professor Fillia Makedon
期刊名称：International Journal of Artificial Intelligence and Expert Systems (IJAE)
电子版ISSN：2180-124X
出版年度：2010
卷号：1
期号：3
页码：54-64
出版社：Computer Science Journals
摘要：In decision theoretic planning, a challenge for Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs) is, many problem domains contain big state spaces and complex tasks, which will result in poor solution performance. We develop a task analysis and modeling (TAM) approach, in which the (PO)MDP model is separated into a task view and an action view. In the task view, TAM models the problem domain using a task equivalence model, with task-dependent abstract states and observations. We provide a learning algorithm to obtain the parameter values of task equivalence models. We present three typical examples to explain the TAM approach. Experimental results indicate our approach can greatly improve the computational capacity of task planning in Markov stochastic domains.
关键词：Markov decision processes; POMDP; task planning; uncertainty; decision-making