Conditional Preference Network with Constraints and Uncertainty

Conditional Preference Network with Constraints and Uncertainty

Sultan Ahmed

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 6422-6423. https://doi.org/10.24963/ijcai.2019/900

In multi-attribute preference-based reasoning, the CP-net is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. This paper outlines three aspects of the CP-net. First, when a CP-net is involved with a set of hard constraints, solving the Constrained CP-net requires dominance testing which is a very expensive operation. We tackle this problem by extending the CP-net model such that dominance testing is not needed. Second, user's choices involve habitual behavior and genuine decision. The former is represented using preferences, while we introduce the notion of comfort to represent the latter. Then, we suggest an extension of the CP-net which can represent both preference and comfort. Third, preferences often come with noise and uncertainty. In this regard, we suggest the probabilistic extension of the Tradeoff-enhanced CP-net (TCP-net) model. The necessary semantics and usefulness of the extensions above are described. Finally, we outline some in-progress and future work.
Keywords:
Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning
Knowledge Representation and Reasoning: Qualitative Reasoning
Uncertainty in AI: Nonprobabilistic Models
Constraints and SAT: Constraint Satisfaction