Preference-based search and multi-criteria optimization

Many real- world AI problems ( e. g., in configuration) are weakly constrained, thus requiring a mechanism for characterizing and finding the preferred solutions. Preference- based search ( PBS) exploits preferences between decisions to focus search to preferred solutions, but does not efficiently treat preferences on global criteria such as the total price or quality of a configuration. We generalize PBS to compute balanced, extreme, and Pareto- optimal solutions for general CSPs, thus handling preferences on and between multiple criteria. A master- PBS selects criteria based on trade- offs and preferences and passes them as an optimization objective to a sub- PBS that performs a constraint- based Branch- and- Bound search. We project the preferences of the selected criterion to the search decisions to provide a search heuristic and to reduce search effort, thus giving the criterion a high impact on the search. The resulting method will be particularly effective for CSPs with large domains that arise if configuration catalogues are large.