Improvement of CSP resolution algorithms using partial orders on domain values

In [Fre91], Freuder defined the notion of interchangeability of values in a CSP, whose purpose is to reduce the size of the problem without losing any solution. He also defined related and weaker notions, such as substitutability. This paper explores this latter notion : firstly the partial pre-orders on domain values induced by the substitutability relation are emphasized ; then these pre-orders are used to improve standard algorithms of resolution such as backtracking or forward-checking, for the task of computing a single solution : the size of the problem is reduced by filtering techniques that preserve the satisfiability. Keywords Constraint Satisfaction Problems, Partial Pre-Orders. 1 Introduction Constraint Satisfaction Problems (CSPs) represent an interesting way of modelization for many Artificial Intelligence and optimization problems. Unfortunately, the resolution of such problems is generally NP-complete. So, in order to facilitate the resolution task, a lot of techniques..

Exploitation de la relation de substituabilité pour la résolution des CSP

: In [FRE 91], Freuder has defined the notion of interchangeability on values in a CSP, whose purpose is to reduce the size of the problem without losing any solution. He also defined related and weaker notions, as subsitutability. This paper explores this latter notion in several ways: first the partial orders on domain values induced by the substitutability relation are emphasized and used to reduce the size of the problem without losing its satisfiability (but some solutions may be lost); finally weaker forms of this notion are presented and used to improve classical resolution methods like backtracking and forward-checking. MOTS-CLE S: Problemes de satisfaction de contraintes, pre-ordres partiels. KEY WORDS: Constraint satisfaction problems, partial pre-orders. 1. Introduction Les problemes de satisfaction de contraintes constituent un outil interessant de mode lisation pour beaucoup de problemes d'intelligence artificielle et de problemes d'optimisation. Malheureusement, de te..