Recent research advances in Reinforcement Learning in Spoken Dialogue Systems

被引：16

作者：

Frampton, Matthew ^{[1
]}

Lemon, Oliver ^{[2
]}

机构：

[1] Stanford Univ, Ctr Study Language & Informat, Stanford, CA 94305 USA

[2] Heriot Watt Univ, Sch Math & Comp Sci, Edinburgh EH14 4AS, Midlothian, Scotland

来源：

KNOWLEDGE ENGINEERING REVIEW | 2009年 / 24卷 / 04期

关键词：

MANAGEMENT;

D O I：

10.1017/S0269888909990166

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper will summarize and analyze the work of the different research groups who have recently made significant contributions In using Reinforcement Learning techniques to learn dialogue strategies for Spoken Dialogue Systems (SDSs). This use of stochastic planning and learning has become an important research area in the past 10 years, since it promises automatic data-driven optimization of the behavior of SDSs that were previously hand-coded by expert developers. We survey the most important developments in the field, compare and contrast the different approaches, and describe current open problems.

引用

页码：375 / 408

页数：34

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