Hybrid Automated Reasoning Tools: from Black-box to Clear-box Integration

TL;DR

This paper evaluates different integration schemas of constraint answer set programming (CASP) methods to identify general principles for developing hybrid automated reasoning tools, aiming to simplify their creation.

Contribution

It provides a case study analyzing various CASP integration schemas to establish foundational principles for hybrid reasoning system development.

Findings

Different integration schemas have distinct strengths and challenges.

Understanding these schemas can guide more efficient hybrid system development.

The study highlights key considerations for combining answer set and constraint programming.

Abstract

Recently, researchers in answer set programming and constraint programming spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming (CASP). CASP languages and systems proved to be largely successful at providing efficient solutions to problems involving hybrid reasoning tasks, such as scheduling problems with elements of planning. Yet, the development of CASP systems is difficult, requiring non-trivial expertise in multiple areas. This suggests a need for a study identifying general development principles of hybrid systems. Once these principles and their implications are well understood, the development of hybrid languages and systems may become a well-established and well-understood routine process. As a step in this direction, in this paper we conduct a case study aimed at evaluating various integration schemas of CASP methods.