Preference-based Problem Solving for Constraint Programming

Abstract

Combinatorial problems such as scheduling, resource allocation, and configuration have many 
attributes that can be subject of user preferences. Traditional optimization approaches compile 
those preferences into a single utility function and use it as the optimization objective when 
solving the problem, but neither explain why the resulting solution satisfies the original
preferences, nor indicate the trade-offs made during problem solving. We argue that the 
whole problem solving process becomes more transparent and controllable for the user
if it is based on the original preferences. We show how the original preferences can be used
to control the problem solving process and how they can be used to explain the choice
and the optimality of the detected solution. Based on this explanation, the user can refine
the preference model, thus gaining full control over the problem solver.