ACLP: Integrating Abduction and Constraint Solving

TL;DR

ACLP is a system that combines abductive reasoning with constraint solving, creating a flexible knowledge representation environment that enhances problem-solving in AI, especially for planning and scheduling tasks.

Contribution

It introduces a novel integration of abductive logic programming with constraint logic programming, improving flexibility and robustness in complex AI problem solving.

Findings

ACLP maintains computational efficiency comparable to direct constraint solving.

The system effectively handles complex planning and scheduling problems.

ACLP adapts easily to changing problem requirements.

Abstract

ACLP is a system which combines abductive reasoning and constraint solving by integrating the frameworks of Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). It forms a general high-level knowledge representation environment for abductive problems in Artificial Intelligence and other areas. In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving facilitating its application to complex problems. The ACLP system is currently implemented on top of the CLP language of ECLiPSe as a meta-interpreter exploiting its underlying constraint solver for finite domains. It has been applied to the problems of planning and scheduling in order to test its computational effectiveness compared with the direct use of the (lower level) constraint solving framework of CLP on which it is built. These experiments provide evidence that the abductive framework of ACLP does not compromise significantly the computational efficiency of the solutions. Other experiments show the natural ability of ACLP to accommodate easily and in a robust way new or changing requirements of the original problem.