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Proceedings Paper

Boltzmann machine generation of initial asset distributions
Author(s): Wael R. Elwasif; Laurene V. Fausett; Sam Harbaugh
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Paper Abstract

Boltzmann machines have been used to solve a variety of combinatorial optimization problems. The use of simulated annealing allows the network to evolve into a state of maximum consensus (or minimum energy) corresponding to an optimal solution of the given problem. In this paper, Boltzmann machine neural networks (without learning) are used to generate initial configurations of assets for a generic game (e.g. chess). The desired distribution of playing pieces is subject to restrictions on the number of pieces (of several different types) that are present, as well as some preferences for the relative positions of the pieces. The rules implemented in the network allow for flexibility in assigning locations for available resources while the probabilistic nature of the network introduces a degree of variability in the solutions generated. The architecture of the network and a method for assigning the weights are described. Results are given for several different examples.

Paper Details

Date Published: 6 April 1995
PDF: 10 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205123
Show Author Affiliations
Wael R. Elwasif, Florida Institute of Technology (United States)
Laurene V. Fausett, Florida Institute of Technology (United States)
Sam Harbaugh, Integrated Software, Inc. (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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