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
C4.5: programs for machine learning
C4.5: programs for machine learning
Depth-first heuristic search on a SIMD machine
Artificial Intelligence
A SIMD approach to parallel heuristic search
Artificial Intelligence
PRA*: massively parallel heuristic search
Journal of Parallel and Distributed Computing
Transposition table driven work scheduling in distributed search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Performance Analysis of Transposition-Table-Driven Work Scheduling in Distributed Search
IEEE Transactions on Parallel and Distributed Systems
Adaptive parallel iterative deepening search
Journal of Artificial Intelligence Research
An evaluation of machine learning in algorithm selection for search problems
AI Communications - The Symposium on Combinatorial Search
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Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of certain aspects of the search process. In this paper we describe the EUREKA system, which combines the benefits of many different approaches to parallel heuristic search. EUREKA uses a machine learning system to decide upon the optimal parallel search strategy for a given problem space. When a new search task is input to the system, EUREKA gathers information about the search space and automatically selects the appropriate search strategy. EUREKA includes diverse approaches to task distribution, load balancing, and tree ordering, and has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results in the fifteen puzzle domain, robot arm path planning domain, and an artificial domain indicate that EUREKA outperforms any existing strategy used exclusively for all problem instances.