Diversity-based inference of finite automata
Journal of the ACM (JACM)
Inference of finite automata using homing sequences
Information and Computation
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Reinforcement Learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning topological maps with weak local odometric information
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Reinforcement learning in POMDPs without resets
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solving POMDPs with continuous or large discrete observation spaces
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The duality of state and observation in probabilistic transition systems
TbiLLC'11 Proceedings of the 9th international conference on Logic, Language, and Computation
Testing probabilistic equivalence through Reinforcement Learning
Information and Computation
Duality in Logic and Computation
LICS '13 Proceedings of the 2013 28th Annual ACM/IEEE Symposium on Logic in Computer Science
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We discuss the problem of finding a good state representation in stochastic systems with observations. We develop a duality theory that generalizes existing work in predictive state representations as well as automata theory. We discuss how this theoretical framework can be used to build learning algorithms, approximate planning algorithms as well as to deal with continuous observations.