Downward refinement and the efficiency of hierarchical problem solving
Artificial Intelligence
Speeding up problem solving by abstraction: a graph oriented approach
Artificial Intelligence - Special volume on empirical methods
Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning
Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Towards a general framework for composing disjunctive and iterative macro-operators
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
ABTWEAK: abstracting a nonlinear, least commitment planner
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Three granular structure models in graphs
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Iterative meta-clustering through granular hierarchy of supermarket customers and products
Information Sciences: an International Journal
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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.