Graphical Models for Game Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning and discovery of predictive state representations in dynamical systems with reset
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Graphical Models in Local, Asymmetric Multi-Agent Markov Decision Processes
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning predictive state representations in dynamical systems without reset
ICML '05 Proceedings of the 22nd international conference on Machine learning
A graphical model for predicting protein molecular function
ICML '06 Proceedings of the 23rd international conference on Machine learning
Relational knowledge with predictive state representations
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Temporal-difference networks for dynamical systems with continuous observations and actions
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Extending context spaces theory by proactive adaptation
ruSMART/NEW2AN'10 Proceedings of the Third conference on Smart Spaces and next generation wired, and 10th international conference on Wireless networking
Learning to make predictions in partially observable environments without a generative model
Journal of Artificial Intelligence Research
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Predictive state representations (PSRs) are models that represent the state of a dynamical system as a set of predictions about future events. The existing work with PSRs focuses on trying to learn exact models, an approach that cannot scale to complex dynamical systems. In contrast, our work takes the first steps in developing a theory of approximate PSRs. We examine the consequences of using an approximate predictive state representation, bounding the error of the approximate state under certain conditions. We also introduce factored PSRs, a class of PSRs with a particular approximate state representation. We show that the class of factored PSRs allow one to tune the degree of approximation by trading off accuracy for compactness. We demonstrate this trade-off empirically on some example systems, using factored PSRs that were learned from data.