Finite-sample convergence rates for Q-learning and indirect algorithms
Proceedings of the 1998 conference on Advances in neural information processing systems II
Reinforcement Learning
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Journal of Artificial Intelligence Research
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Context restoration in multi-tasking dialogue
Proceedings of the 14th international conference on Intelligent user interfaces
Learning effective and engaging strategies for advice-giving human-machine dialogue
Natural Language Engineering
The Knowledge Engineering Review
Importance-Driven Turn-Bidding for spoken dialogue systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning culture-specific dialogue models from non culture-specific data
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: users diversity - Volume Part II
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
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This paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. The specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. The results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input.