CP-nets: From Theory to Practice

• Main Author: Allen, Thomas E.
• Format: Others
• Published: UKnowledge 2016
• Subjects: artificial intelligence; combinatorial preferences; decision making; applications of local search; conditional preference networks; Artificial Intelligence and Robotics
• Online Access: http://uknowledge.uky.edu/cs_etds/42; http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1050&context=cs_etds

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data.