Neural Logic Constraint Solving (original) (raw)

Journal of Parallel and Distributed Computing, 1994

Abstract

ABSTRACT Constraint Satisfaction Problems (CSPs) play a crucial role in Artificial Intelligence and in the real world. CSPs are in general NP-hard, and a general deterministic polynomial time algorithm is not known. CSPs can be reduced in polynomial time to the Satisfaction of a Conjunctive Normal Form (CNF-SAT). We present here techniques for solving CNF-SAT by means of several different simulated neural networks. The results of significant tests are described.

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