Accession Number:

ADA275419

Title:

Neural Network Solutions to Logic Programs with Geometric Constraints

Descriptive Note:

Scientific paper

Corporate Author:

ARMY TOPOGRAPHIC ENGINEERING CENTER FORT BELVOIR VA

Report Date:

1994-01-01

Pagination or Media Count:

15.0

Abstract:

Hybrid knowledge bases HKBs, proposed by Nerode and Subrahmanian, provide a uniform theoretical framework for dealing with the mixed data types and multiple reasoning modes required for solving logical deployment problems. Algorithms based on mixed integer linear programming techniques have been developed for the syntactic subset of HKBs corresponding to function-free Prolog-like logic programs. In this study, we examine the ability of neural networks to solve a more comprehensive set of problems expressed within the hybrid knowledge base framework. The objective of this research is to design and implement a nonlinear optimization procedure for solving extended logic programs with neural networks. We focus upon two types of extensions which are typically required in the formulation of logical deployment problems. The first type of extension, which we shall refer to as a Type I extension, costs of embedding numerical and geometric constraints into logic program it. The second type of extension, which we shall call a Type II extension, consists of incorporating optimization problems into logic clauses.

Subject Categories:

  • Computer Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE