Arc Consistency for CP-Nets under Constraints

Many real world applications require managing both system requirements and user preferences where the latter are usually provided in a qualitative way. We introduce a new approach to handle these two aspects, in an efficient way, respectively through Constraint Satisfaction Problems (CSPs) and CP-nets. In particular, we use Arc Consistency (AC) in order to reduce the search space needed when looking for the optimal outcome in an acyclic CP-net. More precisely, assuming that there are always some shared variables between the CP-net and the CSP, our approach works by first applying AC to the CSP and then update the CP-net with the remaining variables values. The resulting simplified CP-net will then be used to look for the best outcome. Experimental tests conducted on randomly generated problem instances clearly show the effect of AC on the size of the search space and the time needed to find the best outcome.