#### AI Seminar

# Modeling Dynamical Systems with Structured Predictive State Representations

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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.