A multi-level load balancing scheme for OR-parallel exhaustive search programs on the multi-PSI
PPOPP '90 Proceedings of the second ACM SIGPLAN symposium on Principles & practice of parallel programming
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Scalable parallel formulations of depth-first search
Parallel algorithms for machine intelligence and vision
Parallel state-space search for a first solution with consistent linear speedups
International Journal of Parallel Programming
Single-Agent Parallel Window Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unstructured tree search on SIMD parallel computers: a summary of results
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
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
Studying overheads in massively parallel MIN/MAX-tree evaluation
SPAA '94 Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures
PRA*: massively parallel heuristic search
Journal of Parallel and Distributed Computing
Inference bear: designing interactive interfaces through before and after snapshots
Proceedings of the 1st conference on Designing interactive systems: processes, practices, methods, & techniques
An architecture for active networking
HPN '97 Proceedings of the IFIP TC6 seventh international conference on High performance netwoking VII
Integrating user interface agents with conventional applications
IUI '98 Proceedings of the 3rd international conference on Intelligent user interfaces
An architecture for improving the performance of parallel search
An architecture for improving the performance of parallel search
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Machine Learning
Machine Learning
Maximizing the benefits of parallel search using machine learning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Nagging: a scalable fault-tolerant paradigm for distributed search
Artificial Intelligence
E-SETHEO: Design, Configuration and Use of a Parallel Automated Theorem Prover
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Scheduling Methods for Parallel Automated Theorem Proving
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Predicting the Performance of Randomized Parallel Search: An Application to Robot Motion Planning
Journal of Intelligent and Robotic Systems
Automatic generation of search engines
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
Evaluation of a simple, scalable, parallel best-first search strategy
Artificial Intelligence
Hi-index | 0.00 |
Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the 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 the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the EUREKA system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, EUREKA uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. EUREKA has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that EUREKA outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications.