BAYESIAN REINFORCEMENT LEARNING FOR POMDP-BASED DIALOGUE SYSTEMS

被引：0

作者：

Png, ShaoWei ^{[1
]}

Pineau, Joelle ^{[1
]}

机构：

[1] McGill Univ, Sch Comp Sci, Montreal, PQ H3A 1A8, Canada

来源：

2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2011年

关键词：

POMDPs (Partially Observable Markov Decision Processes); Spoken Dialogue; Reinforcement Learning; Bayesian Learning;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

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.

引用

页码：2156 / 2159

页数：4