Fast planning through planning graph analysis
Artificial Intelligence
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Heuristic search + symbolic model checking = efficient conformant planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Asymptotically optimal encodings of conformant planning in QBF
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
Conditional planning in the discrete belief space
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Conformant plans and beyond: Principles and complexity
Artificial Intelligence
Fuzzy Sets and Systems
Mapping conformant planning into SAT through compilation and projection
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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We present a general framework for studying heuristics for planning in the belief space. Earlier work has focused on giving implementations of heuristics that work well on benchmarks, without studying them at a more analytical level. Existing heuristics have evaluated belief states in terms of their cardinality or have used distance heuristics directly based on the distances in the underlying state space. Neither of these types of heuristics is very widely applicable: often goal belief state is not approached through a sequence of belief states with a decreasing cardinality, and distances in the state space ignore the main implications of partial observability. To remedy these problems we present a family of admissible, increasingly accurate distance heuristics for planning in the belief space, parameterized by an integer n. We show that the family of heuristics is theoretically robust: it includes the simplest heuristic based on the state space as a special case and as a limit the exact distances in the belief space.