This book covers the simulation by distributed parallel computers of massively parallel models of interest in artificial intelligence and optimization, bringing together two major areas of current interest within computer science - distributed parallel processing and massively parallel models in artificial intelligence and optimization. Throughout the nine chapters a series of important massively parallel models of computation are surveyed, including cellular automata, Hopfield neural networks, Bayesian networks, Markov random fields, Boltzmann machines, and other "path-following" neural networks with important applications to the solution of mathematical problems. Emphasis is placed on the dynamic behaviour of these models, underlining the importance of discussing algorithmic and programming techniques for their simulation by parallel computers.

Massively Parallel Models of Computation: Distributed Parallel Processing in Artificial Intelligence and Optimization (Ellis Horwood Series in Artif) PDF

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