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AIC Seminar Series

Scalable Planning Under Uncertainty

Daniel BryceArizona State University[Home Page]

Notice:  hosted by Roger Mailler

Date:  2007-05-01 at 10:30

Location:  EJ228  (Directions)

   Abstract

Plan synthesis is a problem in Artificial Intelligence that has been studied by both logicians and decision theorists. Logicians perfect deterministic models to capture and exploit problem structure, leading to the scalability of many classical planners. Decision theorists advocate stochastic models, such as Markov decision processes, which despite their expressiveness remain conspicuously inefficient.

In this talk, I will discuss my efforts toward combining the strengths of logical and probabilistic reasoning techniques for scaling up conditional (contingency) plan synthesis. Specifically, I will describe the adaptation of a popular data structure, called the planning graph, used for heuristic search guidance, from its origins in classical (deterministic) planning to deal with partially observable states and stochastic actions. I will also discuss my preliminary work on a novel formulation of probabilistic planning as multi-objective heuristic search.

   Bio for Daniel Bryce

Daniel Bryce is receiving a Ph.D. in the Computer Science Department at Arizona State University, where he is a member of the Yochan Research Group. His research interests encompass plan life-cycle issues, such as synthesis, execution, explanation, and modeling, in addition to uncertainty in AI. He is also interested in applications of planning to problems facing systems biology, manufacturing, space exploration, and robotics. His work has been published at AAAI, ICAPS, IJCAI, UAI, AI Magazine, AIJ, and JAIR. He also co-delivered tutorials on these topics at ICAPS and IJCAI.

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