Project Description
Learning and Discovery in Dynamical Systems with Hidden State
We consider the problem of learning in dynamical systems with hidden state. This problem is deemed challenging due to the fact that the state is not completely visible to an outside observer. We explore two candidate algorithms: a looping prediction suffix tree algorithm (Isbell & Holmes, 2006), and an algorithm which we call the Merge-Split algorithm, for learning deterministic automata with observations. The latter is based on the work of Gavalda et al. (2006), which approximates a given Hidden Markov Model (HMM) with a learned Probabilistic Deterministic Finite Automaton(PDFA). We aim to discover an algorithm that constructs the most succinct automaton consistent with the data collected from a partially observable system.
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