Making CP-Nets (More) Useful

Authors

  • Thomas Allen University of Kentucky

DOI:

https://doi.org/10.1609/aaai.v28i1.8787

Keywords:

CP-nets, indifference, multivalued features, incomplete preferences

Abstract

Conditional preference networks (CP-nets) exploit the power of conditional ceteris paribus rules to enable a compact representation of human preferences. CP-nets have much appeal. However, the study of CP-nets has not advanced sufficiently for their widespread use in complex, real-world applications. Most studies limit their attention to strict, complete, consistent preferences over binary domains. In my research, I attempt to address these limitations to make CP-nets more useful. I discuss recent research in which we presented a novel algorithm for learning CP-nets from user queries, as well as work showing how to adapt existing algorithms to learn and reason with multivalued CP-nets that can model indifference as well as strict preference. I outline anticipated research to extend our elicitation algorithm to a richer class of CP-nets, develop a formal model of expected flipping sequence length, and learn CP-nets in which a subject prefers to coordinate certain features, leading to a cycle in the dependency graph.

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Published

2014-06-21

How to Cite

Allen, T. (2014). Making CP-Nets (More) Useful. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8787