AIC Seminar Series
Scalable Planning Under Uncertainty
|Daniel Bryce||Arizona State University||[Home Page]|
Notice: hosted by Roger Mailler
Date: 2007-05-01 at 10:30
Location: EJ228 (Directions)
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.
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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