Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison

TL;DR

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

Contribution

It provides a comparative analysis of different logic programming paradigms like constraint logic programming, stable model semantics, and abductive systems.

Findings

Constraint logic programming effectively finds solutions as answer substitutions.

Stable model and abductive systems 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 is constraint logic programming which computes a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are systems based on stable model semantics, abductive systems, and first order logic model generators which compute solutions as models of some theory. This paper compares these different approaches from the point of view of knowledge representation (how declarative are the programs) and from the point of view of performance (how good are they at solving typical problems).