Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
A: an efficient near admissible heuristic search algorithm
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Iterative-deepening-A: an optimal admissible tree search
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Studies in Semi-Admissible Heuristics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Everything you always wanted to know about planning (but were afraid to ask)
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Heuristic search under quality and time bounds
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Search strategies for optimal multi-way number partitioning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Towards rational deployment of multiple heuristics in A*
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Bounded suboptimal search algorithms offer shorter solving times by sacrificing optimality and instead guaranteeing solution costs within a desired factor of optimal. Typically these algorithms use a single admissible heuristic both for guiding search and bounding solution cost. In this paper, we present a new approach to bounded suboptimal search, Explicit Estimation Search, that separates these roles, consulting potentially inadmissible information to determine search order and using admissible information to guarantee the cost bound. Unlike previous proposals, it successfully combines estimates of solution length and solution cost to predict which node will lead most quickly to a solution within the suboptimality bound. An empirical evaluation across six diverse benchmark domains shows that Explicit Estimation Search is competitive with the previous state of the art in domains with unit-cost actions and substantially outperforms previously proposed techniques for domains in which solution cost and length can differ.