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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Granular State Space Search
verfasst von : Jigang Luo, Yiyu Yao
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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Hierarchical problem solving, in terms of abstraction hierarchies or granular state spaces, is an effective way to structure state space for speeding up a search process. However, the problem of constructing and interpreting an abstraction hierarchy is still not fully addressed. In this paper, we propose a framework for constructing granular state spaces by applying results from granular computing and rough set theory. The framework is based on an addition of an information table to the original state space graph so that all the states grouped into the same abstract state are graphically and semantically close to each other.