Bayesian reinforcement learning for POMDP-based dialogue systems

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Bayesian reinforcement learning for POMDP-based dialogue systems
Spoken dialogue systems are gaining popularity with improvements in speech recognition technologies. Dialogue systems can be modeled effectively using POMDPs, achieving improvements in robustness. However, past research on POMDPs-based dialogue system assumes that the model parameters are known. This limitation can be addressed through model-based Bayesian reinforcement learning, which offers a rich framework for simultaneous learning and planning. However, due to the high complexity of the framework, a major challenge is to scale up these algorithms for complex dialogue systems. In this work, we show that by exploiting certain known components of the system, such as knowledge of symmetrical properties, and using an approximate online planning algorithm, we are able to apply Bayesian RL on a realistic spoken dialogue system domain.
ShaoWei Png, Joelle Pineau
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Authors ShaoWei Png, Joelle Pineau
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