Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Compilers: principles, techniques, and tools
Compilers: principles, techniques, and tools
Principles of artificial intelligence
Principles of artificial intelligence
Heuristic search in restricted memory (research note)
Artificial Intelligence
Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
Criticizing solutions to relaxed models yields powerful admissible heuristics
Information Sciences: an International Journal
Improving the efficiency of depth-first search by cycle elimination
Information Processing Letters
Fast recursive formulations for best-first search that allow controlled use of memory
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Conspiracy numbers and caching for searching and/or trees and theorem-proving
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
GA-FreeCell: evolving solvers for the game of FreeCell
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Stratified tree search: a novel suboptimal heuristic search algorithm
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Best-first search algorithms require exponential memory, while depth-first algorithms require only linear memory. On graphs with cycles, however, depth-first searches do not detect duplicate nodes, and hence may generate asymptotically more nodes than best-first searches. We present a technique for reducing the asymptotic complexity of depth-first search by eliminating the generation of duplicate nodes. The automatic discovery and application of a finite state machine (FSM) that enforces pruning rules in a depth-first search, has significantly extended the power of search in several domains. We have implemented and tested the technique on a grid, the Fifteen Puzzle, the Twenty-Four Puzzle, and two versions of Rubik's Cube. In each case, the effective branching factor of the depth-first search is reduced, reducing the asymptotic time complexity.