AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Evolving Real-Time Local Agent Control for Large-Scale Multi-agent Systems
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Heuristic-guided counterexample search in FLAVERS
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
When a decision tree learner has plenty of time
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Graph abstraction in real-time heuristic search
Journal of Artificial Intelligence Research
AWA*-a window constrained anytime heuristic search algorithm
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Best-first utility-guided search
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Limited discrepancy beam search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Heuristic search when time matters
Journal of Artificial Intelligence Research
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We describe a simple technique for converting heuristic search algorithms into anytime algorithms that offer a tradeoff between search time and solution quality. The technique is related to work on use of non-admissible evaluation functions that make it possible to find good, but possibly sub-optimal, solutions more quickly than it takes to find an optimal solution. Instead of stopping the search after the first solution is found, however, we continue the search in order to find a sequence of improved solutions that eventually converges to an optimal solution. The performance of anytime heuristic search depends on the non-admissible evaluation function that guides the search. We discuss how to design a search heuristic that "optimizes" the rate at which the currently available solution improves.