Probabilistic Conditional Preferences

Preferences are ubiquitous in our everyday life.  We use them to take all our decisions, either in isolation or together with others. They change over time, and are affected by our relationship with friends.  We sometimes describe them explicitly, but, most often, we show them implicitly via our actions (like clicks, follows, tweets, blogs, choices).  Being able to faithfully model them and efficiently reason with them is essential in every intelligent environment.  Several formalisms exist to handle preferences of various kinds: qualitative, quantitative, conditional, etc.  However, they usually assume preferences to be certain, and do not provide help for describing and reasoning with uncertain preferences. Uncertainty may be present in a single agent setting, where an individual is not sure about its preferences or some noise is present, and also in a multi-agent setting, where several individuals may have conflicting preferences and uncertainty may be used to reconcile them.  Probabilistic CP-nets (PCP-nets) are a formalism to model conditional qualitative preferences with probabilistic uncertainty. Under reasonable restrictions on the topology of their dependency graph, it is computationally easy to perform various tasks in PCP-nets. Thus, they provide an efficient tool to model and reason with preferences.  In this talk, I will describe PCP-nets and how to respond to optimality and dominance queries over them. I will then advocate for the need of a logical language to model them, and hint at their possible use in various scenarios.  Finally, I will compare them to other frameworks, and will discuss preference elicitation/learning issues.