Some stochastic models like Markov decision process (MDP) are used to model the dialogue manager. MDP-based system degrades fast when uncertainty about user¡6s intention increases. We propose a novel dialogue model based on the partially observable Markov decision process (POMDP). We use hidden system states and user intentions as the state set, parser results and low-level information as the observation set, domain actions and dialogue repair actions as the action set. Here the low-level information is extracted from different input modals using Bayesian networks. Because of the limitation of exact algorithms, we focus on heuristic methods and their applicability in dialogue management.

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Bibliographic reference.  Zhang, Bo / Cai, Qingsheng / Mao, Jianfeng / Chang, Eric / Guo, Baining (2001): "Spoken dialogue management as planning and acting under uncertainty", In EUROSPEECH-2001, 2169-2172.