Relational agents: a model and implementation of building user trust
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding unsegmented user utterances in real-time spoken dialogue systems
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Natural behavior of a listening agent
Lecture Notes in Computer Science
Overview of the DARPA Speech and Natural Language Workshop
HLT '89 Proceedings of the workshop on Speech and Natural Language
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
A casual conversation system using modality and word associations retrieved from the web
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Analysis of listening-oriented dialogue for building listening agents
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Design targeting voice interface robot capable of active listening
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Learning to control listening-oriented dialogue using partially observable markov decision processes
ACM Transactions on Speech and Language Processing (TSLP)
Reward shaping for statistical optimisation of dialogue management
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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This paper investigates how to automatically create a dialogue control component of a listening agent to reduce the current high cost of manually creating such components. We collected a large number of listening-oriented dialogues with their user satisfaction ratings and used them to create a dialogue control component using partially observable Markov decision processes (POMDPs), which can learn a policy to satisfy users by automatically finding a reasonable reward function. A comparison between our POMDP-based component and other similarly motivated systems using human subjects revealed that POMDPs can satisfactorily produce a dialogue control component that can achieve reasonable subjective assessment.