AIC Seminar Series

This talk will present a new efficient algorithm for probabilistic plan recognition, based on a model of plan execution. This algorithm is capable of handling a large class of domains including partially observable domains and domains where the agent abandons goals.

Dr. Geib is a Senior Research Scientist in the Automated Reasoning group at Honeywell Labs. He is the principle author of the Probabilistic Hostile Agent Task Tracker (PHATT) Software and theory, and has published papers on the theory and application of this work. Dr. Geib's current research interests also include Artificial Intelligence(AI) planning methods and the handling of uncertainty in AI planning methods. His Ph.D. thesis work concerned the use of hierarchical incremental planning for uncertain domains. Since then, Dr. Geib has worked on a number of research projects involving action under uncertainty. He has continued to look at the issues presented by uncertain domains using decision theoretic methods to extend hierarchical planning and general AI methods of abstraction for problems formulated using standard decision theory. Dr. Geib has been at Honeywell Labs since 1997. Prior to this, he was a post-doctoral fellow at the University of British Columbia.

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