The role of the user's domain knowledge in generation
Computational Intelligence
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
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
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Intention Reconciliation by Collaborative Agents
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Enhancing the Interaction between Agents and Users
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Decision theoretic dialogue planning for initiative problems
UM'05 Proceedings of the 10th international conference on User Modeling
MITS: a mixed-initiative intelligent tutoring system for sudoku
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
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In this paper, we address the problem of providing guidelines to designers of mixed-initiative artificial intelligence systems, which specify when the system should take the initiative to solicit further input from the user, in order to carry out a problem solving task. We first present a utility-based quantitative framework which is dependent on modeling: whether the user has the knowledge the system is seeking, whether the user is willing to provide that knowledge and whether the user would be capable of understanding the request for information from the system. Examples from the application of sports scheduling are included. We also discuss a qualitative version of the model, for applications with sparse data. This paper demonstrates a novel use for user models, one in which the system does not simply alter its generation based on the user model, but in fact makes a user-specific decision about whether to interact at all.