Abstract
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.
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References
Bacchus, F., Yang, Q.: Downward refinement and the efficiency of hierarchical problem solving. Artificial Intelligence 71, 43–100 (1994)
Holte, R.C., Mkadmi, T., Zimmer, R.M., MacDonald, A.J.: Speeding up problem solving by abstraction: a graph oriented approach. Artificial Intelligence 85, 321–361 (1996)
Knoblock, C.A.: Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning. Kluwer Academic Publishers, Boston (1993)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasonging about Data. Kluwer Academic Publishers, Boston (1991)
Sacerdoti, E.D.: Planning in a hierarchy of abstraction spaces. Artificial Intelligence 5, 115–135 (1974)
Shell, P., Carbonell, J.: Towards a general framework for composing disjunctive and iterative macro-operators. In: Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 596–602 (1989)
Yang, Q., Tenenberg, J.D.: Abtweak: Abstracting a nonlinear, least commitment planner. In: Proceedings of the Eighth National Conference on Artificial Intelligence, pp. 204–209 (1990)
Yao, Y.Y.: Artificial intelligence perspectives on granular computing. In: Pedrycz, W., Chen, S.H. (eds.) Granular Computing and Intelligent Systems. Springer, Berlin (2011)
Yao, Y.Y.: A unified framework of granular computing. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 401–410. Wiley, New York (2008)
Yao, Y.Y.: Granular computing: past, present and future. In: 2008 IEEE International Conference on Granular Computing, pp. 80–85 (2008)
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Luo, J., Yao, Y. (2011). Granular State Space Search. In: Butz, C., Lingras, P. (eds) Advances in Artificial Intelligence. Canadian AI 2011. Lecture Notes in Computer Science(), vol 6657. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21043-3_35
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DOI: https://doi.org/10.1007/978-3-642-21043-3_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21042-6
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