Planning and acting in partially observable stochastic domains
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Algorithms for sequential decision-making
Algorithms for sequential decision-making
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Multiagent coordination by Extended Markov Tracking
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An argumentation based approach for practical reasoning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Dynamics based control with an application to area-sweeping problems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A hybrid controller based on the egocentric perceptual principle
Robotics and Autonomous Systems
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We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version of the DBC for PSR model, EMT-PSR, and demonstrate how this algorithm can be applied to solve several control problems. We then provide some classifications and requirements of PSR environment models that are necessary for the EMT-PSR algorithm to operate.