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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, consists of embedding numerical and geometric constraints into logic programs. The second type of extension, which we shall call a Type II extension, consists of incorporating optimization problems into logic clauses. © 1993 SPIE. All rights reserved.

1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/006-720-998-107-50X tex.type: [object Object]