Making systems sensitive to the user's time and working memory constraints
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Using plan recognition in human-computer collaboration
UM '99 Proceedings of the seventh international conference on User modeling
Empirically evaluating an adaptable spoken dialogue system
UM '99 Proceedings of the seventh international conference on User modeling
Resource-Adaptive Action Planning in a Dialogue System for Repair Support
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Evaluating response strategies in a Web-based spoken dialogue agent
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Learning optimal dialogue strategies: a case study of a spoken dialogue agent for email
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Learning effective and engaging strategies for advice-giving human-machine dialogue
Natural Language Engineering
AIMHelp: generating help for GUI applications automatically
Proceedings of the 10th International Conference NZ Chapter of the ACM's Special Interest Group on Human-Computer Interaction
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
Experience breeding in process-aware information systems
CAiSE'13 Proceedings of the 25th international conference on Advanced Information Systems Engineering
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The learning and self-adaptive capability in dialog systems has become increasingly important with the advances in a wide range of applications. For any application, particularly the one dealing with a technical domain, the system should pay attention to not only the user experience level and dialog goals, but more importantly, the mechanism to adapt the system behavior to the evolving state of the user. This paper describes a methodology that first identifies the user experience level and utility metrics of the goal and sub-goals, then automatically adjusts those parameters based on discourse history and thus directs adaptive dialog management.