Hard and soft constraints for reasoning about qualitative conditional preferences
Journal of Heuristics
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
The computational complexity of dominance and consistency in CP-Nets
Journal of Artificial Intelligence Research
Graphical representation of ordinal preferences: languages and applications
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Qualitative preference-based service selection for multiple agents
Web Intelligence and Agent Systems
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CP-net (Conditional Preference Network) is one of the extensively studied languages for representing and reasoning with preferences. The fundamental operation of dominance testing in CP-nets, i.e. determining whether an outcome is preferred to another, is very important in many real-world applications. Current techniques for solving general dominance queries is to search for improving flipping sequence from one outcome to another as a proof of the dominance relation in all rankings satisfying the given CP-net. However, it is generally a hard problem even for binary-valued, acyclic CP-nets and tractable search algorithms exist only for specific problem classes. Hence, there is a need for efficient algorithms and techniques for dominance testing in more general problem settings. In this paper, we propose a heuristic approach, called DT*, to dominance testing in arbitrary acyclic multi-valued CP-nets. Our proposed approach guides the search process efficiently and allows significant reduction of search effort without impacting soundness or completeness of the search process. We present results of experiments that demonstrate the computational efficiency and feasibility of our approach to dominance testing.