• Calendar

December 25, Thursday
12:00 – 14:00

Consider the problem of providing users with tools that enable optimal selection of configurable products, such as, vacation packages, personal computers, or even a personalized newspaper. These configuration problems can be formulated abstractly as constrained-optimization problems: Find the best (given the user?s preferences) vacation satisfying various constraints (duration, price, etc.).

This class of constrained optimization problem, which we refer to as "preference-based constrained optimization" differs from the classical constrained optimization problems studied in applied mathematics and OR. Rather than real-valued variables we are mostly dealing with discrete variables with finite domains, and instead of real-value objective functions, we have the amorphous notion of "user preferences". To formulate this class of problems properly, we need to more carefully specify their input, bearing in mind that this information must be realistically obtainable from the type of users we have in mind. We must also provide efficient solution algorithms, so that this approach is usable in on-line applications.

In this talk I will describe CP-networks, a knowledge-representation structure that exploits the notion of conditional preferential independence to enable convenient preference elicitation and representation. I will explain their basic properties and I will show how we can do preference-based constrained optimization given a CP-net efficiently. To illustrate these ideas, I will describe and demonstrate a tool we recently built for personalization of synchronized rich-media presentations