Boltzmann machine generation of initial asset distributions

Wael R. Elwasif; Laurene V. Fausett; Sam Harbaugh

doi:10.1117/12.205123

6 April 1995 Boltzmann machine generation of initial asset distributions

Wael R. Elwasif, Laurene V. Fausett, Sam Harbaugh

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205123
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

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Wael R. Elwasif, Laurene V. Fausett, and Sam Harbaugh "Boltzmann machine generation of initial asset distributions", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205123

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KEYWORDS

Neural networks

Network architectures

Algorithms

Annealing

Applied mathematics

Chemical elements

Chlorine

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