Trainbot: a Spoken Dialog Sytem Using Partially Observable Markov Decision Processes

摘要：

Due to speech recognition and understanding errors, spoken dialog systems have been suffering from inherent uncertainty in the whole conversation. Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modeling the inherent uncertainty in spoken dialogue systems. This Paper describes a dialog system, Trainbot, which uses a POMDP statistical-based dialog model updating information states and making appropriate dialog strategies in a given situation.

关键词： Statistical dialog system dialog management POMDP

作者: Weidong Zhou Baozong, Yuan

作者单位: Institute of Information Science, Beijing Jiaotong University, Beijing, China, 100044 College of Inf Institute of Information Science, Beijing Jiaotong University, Beijing, China, 100044

会议类型: 国际会议

会议名称: 2010 The IET 3rd International Conference on Wireless,Mobile & Multimedia Networks(第三届IET无线移动及多媒体网络国际会议 ICWMMN 2010)

会议地点: 北京

会议语种:英文

页码: 381-384

在线出版日期: 2010-09-26（万方平台首次上网日期，不代表论文的发表时间）