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AIC Seminar Series

A Framework for Constraint Reasoning with Uncertain Data

Neil Yorke-SmithImperial College, London[Home Page]

Date:  2004-07-19 at 10:00

Location:  EJ228  (Directions)

   Abstract

Uncertainty is a common feature of real-life combinatorial optimization problems. Constraint programming is an AI approach that has developed into a powerful tool for tackling these problems, but constraint programming with uncertain data can lead us to solve the wrong problem because of the approximations made. This outcome is of little help to a user who expects the right problem to be tackled and reliable information returned. In this talk we present the certainty closure, a framework for reliable constraint reasoning in the presence of uncertain data. We describe how to provide the user with reliable insight, by enclosing the uncertainty using what is known for sure about the data, and then deriving a closure, a set of potential solutions to the uncertain constraint problem. We outline the formal basis of the framework, and illustrate the benefits on two case studies in network optimization and aerospace planning. This is joint work with Carmen Gervet.

   Bio for Neil Yorke-Smith

Neil Yorke-Smith received his Ph.D. in Artificial Intelligence from Imperial College London in June 2004. His thesis research focused on handling uncertainty in constraint-based reasoning. During summer 2003, Neil visited the NASA Ames Research Center, where he worked on probabilistic reasoning for temporal planning.

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