Solving conditional and composite constraint satisfaction problems

• Authors: Malek Mouhoub;Amrudee Sukpan
• Venue: Proceedings of the 2007 ACM symposium on Applied computing
• Year: 2007

Constraint Satisfaction Problems (CSPs) have been widely used to solve combinatorial problems. In order to deal with dynamic CSPs where the information regarding any possible change is known a priori and can thus be enumerated beforehand, conditional constraints and composite variables have been studied in the past decade. Indeed, these two concepts allow the addition of variables and their related constraints in a dynamic manner during the resolution process. More precisely, a conditional constraint restricts the participation of a variable in a feasible scenario while a composite variable allows us to express a disjunction of variables where only one will be added to the problem to solve. In this paper we introduce a unique CSP framework including conditional constraints and composite variables. We call this model, a Conditional and Composite CSP (or CCCSP). In order to solve a CCCSP, we propose two methods respectively based on Stochastic Local Search (SLS) and backtrack search with constraint propagation. The experimental comparison of these two methods, on randomly generated consistent CCCSPs, demonstrates the efficiency of the exact method based on constraint propagation in the case of middle and under constrained problems while the SLS based method is the technique of choice for highly constrained problems and also in case we want to trade search time for the quality of the solution returned (number of solved constraints).