A Comparison of Logic Programming Approaches for Representation and Solving of Constraint Satisfaction Problems

TL;DR

This paper compares various logic programming approaches for representing and solving constraint satisfaction problems, focusing on their declarative expressiveness and problem-solving performance.

Contribution

It provides a comparative analysis of different logic programming paradigms, highlighting their strengths and weaknesses in knowledge representation and efficiency.

Findings

Definite and constraint logic programs effectively compute solutions as answer substitutions.

Stable model, abduction, and first-order logic approaches generate solutions as models of theories.

Performance varies significantly across approaches depending on problem complexity.

Abstract

Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there are definite programs and constraint logic programs that compute a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are approaches based on stable model semantics, abduction, and first-order logic model generation that compute solutions as models of some theory. This paper compares these different approaches from point of view of knowledge representation (how declarative are the programs) and from point of view of performance (how good are they at solving typical problems).