Document Type

Article

Publication Date

7-2016

Publication Title

Artificial Intelligence

Volume

236

First Page

65

Last Page

89

Abstract

Integrating diverse formalisms into modular knowledge representation systems offers increased expressivity, modeling convenience, and computational benefits. We introduce the concepts of abstract inference modules and abstract modular inference systems to study general principles behind the design and analysis of model generating programs, or solvers, for integrated multi-logic systems. We show how modules and modular systems give rise to transition graphs, which are a natural and convenient representation of solvers, an idea pioneered by the SAT community. These graphs lend themselves well to extensions that capture such important solver design features as learning. In the paper, we consider two flavors of learning for modular formalisms, local and global. We illustrate our approach by showing how it applies to answer set programming, propositional logic, multi-logic systems based on these two formalisms and, more generally, to satisfiability modulo theories.

Comments

© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Funded by the University of Nebraska at Omaha Open Access Fund