Abstract:
Preference handling is a problem of much theoretical and practical interest. In planning, preferences arises naturally when one considers richer notions of goals, as well as over-subscribed planning problems. In knowledge representation, it is a core issue with much recent work on preference languages and algorithms. In system design, preferences can be used to control choices and provide a personalized experience or adapt to varying context. In this talk I will discuss some of my work, together with many colleagues, in these areas. I will consider some of the challenges we face when designing a preference specification formalism and describe a simple graphical input language, CP-nets - which attempts to address some of these challenges. Surprisingly, CP-nets are closely related to an important analysis tool in planning - the causal graph, and the problem of inference in these networks has important links to the question of the complexity of plan generation. Moreover, the problem of finding a preferred plan given a rich goal specification can be solved by using techniques developed for constrained optimization in CP-nets. But CP-network are inherently a propositional specification language, whereas many control applications require a relational language. Time permitting, I will explain why this problem arises naturally in intelligent control applications. I will show how some recent and richer relational languages can be used to address this problem, and how closely they are related to probabilistic relational models.