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
Single-Agent Parallel Window Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Trends in Cooperative Distributed Problem Solving
IEEE Transactions on Knowledge and Data Engineering
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
Iterative Grid-Based Computing Using Mobile Agents
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
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Today, mobile and smart phones are often viewed as enablers of pervasive computing systems because they provide anytime and anywhere access to information services and computational resources. However, mobile devices are inherently constrained in their computational power and battery capacity making them mere "dumb terminals" connected to a resource-rich pervasive environment. If they are ever to play a more prominent role as true elements of a pervasive environment, mobile devices must be able to embed more application logic and delegate processing requests to pervasive infrastructure. In this paper we discuss distribution and offloading of computationally intensive tasks in pervasive environments populated by mobile devices. This approach is illustrated by experimenting with a distributed version of iterative deepening A* search algorithm. In our approach, the solution space of a problem being solved is partitioned and distributed among heterogeneous mobile devices, which yields a significant increase in the time of finding an optimal solution. Distributed IDA* search algorithm does not require any coordination or communication between mobile devices, but added inter-processor communication through shared memory further increases the efficiency of the algorithm. This paper presents the results of our experiments with the algorithm and discusses a number of issues related to its implementation.