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cs.LG2009
Closing the Learning-Planning Loop with Predictive State Representations
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
A central problem in artificial intelligence is that of planning to maximize future reward under uncertainty in a partially observable environment. In this paper we propose and dem…
cs.LG2009
Reduced-Rank Hidden Markov Models
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
We introduce the Reduced-Rank Hidden Markov Model (RR-HMM), a generalization of HMMs that can model smooth state evolution as in Linear Dynamical Systems (LDSs) as well as non-log-…