Representing and Learning Propositional Logic in Symmetric Connectionist Networks

Authors

Gadi Pinkas, Washington University in St Louis

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

1993-01-01

Filename

WUCS-93-9.PDF

Technical Report Number

WUCS-93-9

Abstract

The chapter presents methods for efficiently representing logic formulas in connectionist networks that perform energy minimization. Algorithms are given for transforming any formula into a network in linear time and space and for learning representations of unknown formulas by observing examples of satisfying truth assignments. The relaxation process that underlies networks of energy minimization reveals an efficient hill climbing algorithm for satisfiability problems. Experimental results indicate that the parallel implementation of the algorithm with give extremely good average-case performance, even for large-scale, hard satisfiability problems (randomly generated).

Comments

Permanent URL: http://dx.doi.org/10.7936/K7DB802B

Recommended Citation

Pinkas, Gadi, "Representing and Learning Propositional Logic in Symmetric Connectionist Networks" Report Number: WUCS-93-9 (1993). All Computer Science and Engineering Research.
https://openscholarship.wustl.edu/cse_research/330