Mechanising shared configuration and diagnosis theories through constraint logic programming

Abstract

Configuration and diagnosis problem-solvers are commonly championed as successes of applied artificial intelligence techniques. A common problem is that problem-solvers typically encode task-specific representation assumptions and simplifications in their domain theories, hindering the reuse of the domain theories between the problem-solvers. While model-based reasoning techniques have been shown to provide an interesting approach to sharing component and device specifications, their respective mechanisations are generally too inefficient. We show how constraint logic programming languages provide a flexible environment in which constraint-based specifications can be effectively shared and efficiently mechanised by exploiting constraint solving and propagation techniques tightly integrated with the backtracking search mechanism of logic programming languages. A component specification language is presented and the mappings from the language to the constraint system and strategies for guiding the search are defined for the respective problem-solvers.