Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Parallel depth first search. Part I. implementation
International Journal of Parallel Programming
Tree search and ARC consistency in constraint satisfaction algorithms
Search in Artificial Intelligence
Scalable parallel formulations of depth-first search
Parallel algorithms for machine intelligence and vision
Single-Agent Parallel Window Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear-space best-first search
Artificial Intelligence
Criticizing solutions to relaxed models yields powerful admissible heuristics
Information Sciences: an International Journal
BIDA: an improved perimeter search algorithm
Artificial Intelligence
An Improved Bidirectional Heuristic Search Algorithm
Journal of the ACM (JACM)
Bidirectional Heuristic Search Again
Journal of the ACM (JACM)
Journal of Systems and Software
Bidirectional heuristic search reconsidered
Journal of Artificial Intelligence Research
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Since its introduction three decades ago, bidirectional heuristic search did not deliver the expected performance improvement over unidirectional search methods. The problem of search frontiers passing each other is a widely accepted conjecture led to amendments to steer the search using computationally demanding heuristics. The computation cost associated with front-to-front evaluations crippled further investigation and hence bidirectional search was long neglected. However, recent findings demonstrate that the initial conjecture is wrong since the major search effort is spent after the frontiers have already met [7]. In this paper we reconsider bidirectional search by proposing a new generic approach based on cluster computing. The proposed approach is then evaluated and compared with its unidirectional counterparts. The obtained results reveal that cluster computing is a viable approach for distributed heuristic search. Particularly, clustered bidirectional search is capable of solving problems beyond unidirectional search capabilities and in the same time outperforms unidirectional approaches in terms of memory space and execution time.