AI Seminar

Modeling Dynamical Systems with Structured Predictive State Representations

Britton Wolfe

PhD Candidate, Computer Science and Engineering
University of Michigan
Tuesday, March 03, 2009
4:00pm - 5:30pm
Stained-Glass Conference Room (3725 Beyster Bldg.)

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About the Event

Predictive state representations (PSRs) are a class of models that represent the state of a dynamical system (e.g. an agent's environment) as a set of predictions about future events. PSRs are capable of representing partially observable, stochastic dynamical systems, including any system that can be modeled by a finite partially observable Markov decision process (POMDP). There is evidence that predictive state is useful for generalization and helps to learn accurate models. This talk will focus upon two classes of PSR models, factored PSRs and multi-modal PSRs, which exploit different types of structure in a dynamical system in order to scale up PSR models to large systems. The factored PSR exploits conditional independence, allowing a trade-off between model compactness and accuracy. The multi-modal PSR is designed for systems that switch between different modes of operation; the model makes specialized predictions for each mode. The model also maintains predictions about the current mode of the system, because the current mode is only observable after some delay. Both the factored PSR and the multi-modal PSR were evaluated on the task of predicting highway traffic on a six-lane portion of Interstate 80. The learned PSR models compare favorably with other prediction techniques, achieving an average error as low as one car length when predicting the distance a car will travel over five seconds.

Additional Information

Sponsor(s): Toyota AI Seminar Series

Open to: Public