Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Decision-theoretic troubleshooting
Communications of the ACM
Planning and acting in partially observable stochastic domains
Artificial Intelligence
An improved policy iteration algorithm for partially observable MDPs
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Planning and Acting under Uncertainty: A New Model for Spoken Dialogue System
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
An architecture for a generic dialogue shell
Natural Language Engineering
Towards developing general models of usability with PARADISE
Natural Language Engineering
Quantitative and qualitative evaluation of Darpa Communicator spoken dialogue systems
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
Spoken language communication with machines: the long and winding road from research to business
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Task-based dialog management using an agenda
ANLP/NAACL-ConvSyst '00 Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems - Volume 3
The Knowledge Engineering Review
Error handling in the RavenClaw dialog management framework
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Comparing the utility of state features in spoken dialogue using reinforcement learning
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Personalizing influence diagrams: applying online learning strategies to dialogue management
User Modeling and User-Adapted Interaction
Applying POMDPs to dialog systems in the troubleshooting domain
NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
Technical support dialog systems: issues, problems, and solutions
NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
Journal of Artificial Intelligence Research
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
Are We There Yet? Research in Commercial Spoken Dialog Systems
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Evaluation of a hierarchical reinforcement learning spoken dialogue system
Computer Speech and Language
Statistical dialog management methodologies for real applications
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Self-adapting TV based applications
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: design for all and eInclusion - Volume Part I
Learning automata-based approach to learn dialogue policies in large state space
International Journal of Intelligent Information and Database Systems
Multimodal interface model for socially dependent people
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
Robust Design and Control of Call Centers with Flexible Interactive Voice Response Systems
Manufacturing & Service Operations Management
Journal of Ambient Intelligence and Smart Environments - A software engineering perspective on smart applications for AmI
Towards situated collaboration
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
A mixed-initiative conversational dialogue system for healthcare
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Inverse reinforcement learning for interactive systems
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
Bringing context-aware access to the web through spoken interaction
Applied Intelligence
Reward shaping for statistical optimisation of dialogue management
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
A domain-independent statistical methodology for dialog management in spoken dialog systems
Computer Speech and Language
Providing personalized Internet services by means of context-aware spoken dialogue systems
Journal of Ambient Intelligence and Smart Environments - Context Awareness
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In designing a spoken dialogue system, developers need to specify the actions a system should take in response to user speech input and the state of the environment based on observed or inferred events, states, and beliefs. This is the fundamental task of dialogue management. Researchers have recently pursued methods for automating the design of spoken dialogue management using machine learning techniques such as reinforcement learning. In this paper, we discuss how dialogue management is handled in industry and critically evaluate to what extent current state-of-the-art machine learning methods can be of practical benefit to application developers who are deploying commercial production systems. In examining the strengths and weaknesses of these methods, we highlight what academic researchers need to know about commercial deployment if they are to influence the way industry designs and practices dialogue management.