Young, S and Gašić, M and Thomson, B and Williams, JD (2013) POMDP-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 101. pp. 1160-1179. ISSN 0018-9219Full text not available from this repository.
Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech recognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a framework. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article provides an overview of the current state of the art in the development of POMDP-based spoken dialog systems. © 1963-2012 IEEE.
|Uncontrolled Keywords:||Belief monitoring partially observable Markov decision process (POMDP) policy optimization reinforcement learning spoken dialog systems (SDSs)|
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||16 Jul 2015 13:19|
|Last Modified:||28 Nov 2015 06:50|