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AI Seminar

Finding Preferable Choices in Constrained and Multiagent Contexts

Ed DurfeeProfessorComputer Science and Engineering, University of Michigan

Motivated by problems that arise in coordinating people with cognitive
difficulties, we have been investigating techniques for finding individual
and group choices that are (arguably) most preferable given constraints.
In particular, we have been studying possibilities and limitations in
finding preferred outcomes based only on qualitative information about
preferences, captured in CP-nets. Further, there can be constraints on
allowable combinations of features of outcomes, which introduces further
complications to the search process. In this talk, I will summarize the
motivations and assumptions behind our efforts, describe the underlying
representations upon which our work is based, and discuss the algorithms
that we have developed, along with our evaluations of their performance,
highlighting lessons learned and remaining open problems.

Sponsored by

Toyota AI Seminar Series