Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Affective computing
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Spoken dialogue technology: enabling the conversational user interface
ACM Computing Surveys (CSUR)
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Inferring user goals from personality and behavior in a causal model of user affect
Proceedings of the 8th international conference on Intelligent user interfaces
Conversation as Action Under Uncertainty
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
How to find trouble in communication
Speech Communication - Special issue on speech and emotion
Modeling drivers' speech under stress
Speech Communication - Special issue on speech and emotion
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Integrating Emotional Cues into a Framework for Dialogue Management
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
The Knowledge Engineering Review
User modeling and adaptation in health promotion dialogs with an animated character
Journal of Biomedical Informatics - Special issue: Dialog systems for health communications
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Active affective State detection and user assistance with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Investigating clarification strategies in a hybrid POMDP dialog manager
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
ACM Transactions on Speech and Language Processing (TSLP)
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We propose a novel approach to developing a tractable affective dialogue model for probabilistic frame-based dialogue systems. The affective dialogue model, based on Partially Observable Markov Decision Process (POMDP) and Dynamic Decision Network (DDN) techniques, is composed of two main parts: the slot-level dialogue manager and the global dialogue manager. It has two new features: (1) being able to deal with a large number of slots and (2) being able to take into account some aspects of the user's affective state in deriving the adaptive dialogue strategies. Our implemented prototype dialogue manager can handle hundreds of slots, where each individual slot might have hundreds of values. Our approach is illustrated through a route navigation example in the crisis management domain. We conducted various experiments to evaluate our approach and to compare it with approximate POMDP techniques and handcrafted policies. The experimental results showed that the DDN–POMDP policy outperforms three handcrafted policies when the user's action error is induced by stress as well as when the observation error increases. Further, performance of the one-step look-ahead DDN–POMDP policy after optimizing its internal reward is close to state-of-the-art approximate POMDP counterparts.