Abstract
Due to speech recognition and understanding errors, spoken dialog systems have been suffering from inherent uncertainty in the whole conversation. Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modeling the inherent uncertainty in spoken dialogue systems. This Paper describes a dialog system, Trainbot, which uses a POMDP statistical-based dialog model updating information states and making appropriate dialog strategies in a given situation.
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