AIC Seminar Series

Activity monitoring is often a necessary ability for systems that assist people with various tasks. In general, activity monitoring can be performed by using a stream of sensor data and a model of a person’s behavior and environment to infer which actions have been performed and the current state of the environment. When the available sensor data is limited, the monitoring task must rely heavily on the temporal aspects of the behavior model. However, with current modeling formalisms, such as Dynamic Bayesian Networks, it is difficult to represent complex temporal relationships. I will show how to extend these formalisms to represent and efficiently reason about complex, quantitative temporal relationships and describe a parameterization strategy that allows easy specification of the model.

Bart Peintner is currently in the midst of a two month visit to SRI. He is a Ph.D. candidate in computer science at the University of Michigan, studying in the AI laboratory under the direction of Martha Pollack. His main interest is in automated temporal reasoning, both probabilistic and deterministic. He received an M.S. in computer science at the University of Michigan, and a B.S. in Electrical Engineering at Kansas State University.

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