Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Prolog programming for artificial intelligence
Prolog programming for artificial intelligence
Heuristic search in restricted memory (research note)
Artificial Intelligence
Artificial Intelligence
Reducing reexpansions in iterative-deepening search by controlling cutoff bounds
Artificial Intelligence
An efficient iterative threshold heuristic tree search algorithm
An efficient iterative threshold heuristic tree search algorithm
Efficient memory-bounded search methods
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
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
Learning subgoal sequences for planning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Learning heuristic functions for large state spaces
Artificial Intelligence
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 is a general search algorithm that always expands next a frontier node of lowest cost. Its applicability, however, is limited by its exponential memory requirement. Iterative deepening, a previous approach to this problem, does not expand nodes in best-first. order if the cost. function can decrease along a path. We present a linear-space best-first search algorithm (RBFS) that always explores new nodes in best-first order, regardless of the cost function, and expands fewer nodes than iterative deepening with a nondecreasing cost function. On the sliding-tile puzzles, RBFS with a weighted evaluation function dramatically reduces computation time with only a small penalty in solution cost. In general, RBFS reduces the space complexity of best-first search from exponential to linear, at the cost of only a constant factor in time complexity in our experiments.