2007 · 118 citations · 10 references
ComputingComputer ScienceMarkov Decision ProcessesNatural Language ProcessingUncertainty QuantificationDecision-making Under UncertaintyDialogue SystemsHidden Markov ModelsMarkov ProcessesPartially Observable Markov Decision ProcessesUncertainty RepresentationBelief StatesDialog SystemsDialogue ManagementHidden Information ModelsPolicy Search For PomdpsSpeech UnderstandingSpoken Dialogue Systems
Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for modelling the uncertainty inherent in spoken dialog systems. However, conventional POMDPs scale poorly with the size of state and observation space. This paper describes a variation of the classic POMDP called the hidden information state (HIS) model in which belief distributions are represented efficiently by grouping states together into partitions and policy optimisation is made tractable by using a master to summary space mapping. An implementation of the HIS model is described for a Tourist Information application and aspects of its training and operation are illustrated.
10