Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Parallel depth first search. Part I. implementation
International Journal of Parallel Programming
Parallel depth first search. Part II. analysis
International Journal of Parallel Programming
Single-Agent Parallel Window Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
BIDA: an improved perimeter search algorithm
Artificial Intelligence
Journal of Systems and Software
Disjoint pattern database heuristics
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Enhanced Iterative-Deepening Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
A parallel implementation of iterative-deepening-A
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
Parallel Branch-and-Bound Formulations for AND/OR Tree Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we investigate the performance of distributed heuristic search methods based on a well-known heuristic search algorithm, the iterative deepening A^* (IDA^*). The contribution of this paper includes proposing and assessing a distributed algorithm for IDA^*. The assessment is based on space, time and solution quality that are quantified in terms of several performance parameters such as generated search space and real execution time among others. The experiments are conducted on a cluster computer system consisting of 16 hosts built around a general-purpose network. The objective of this research is to investigate the feasibility of cluster computing as an alternative for hosting applications requiring intensive graph search. The results reveal that cluster computing improves on the performance of IDA^* at a reasonable cost.