A utility-based theory of initiative in mixed-initiative systems

In this paper, we present a utility-based decision making process to be used by a system in determining when to take the initiative to interact with a user, in a mixed-initiative artificial intelligence system. The decision making is based on a calculation of the expected utility of various courses of action and the likelihood that the user will be an effective contributor of information, if an interaction were initiated. We demonstrate the model in the application of sports scheduling, but discuss its potential for other applications as well. In particular, we contrast with existing work on utility-based reasoning in environments of agent-agent interaction. Our overall conclusion is that there is value to adopting an explicit reasoning process about taking the initiative as part of the overall deliberation about problem solving in collaborative environments.

[1]  Craig Boutilier,et al.  Sequential Optimality and Coordination in Multiagent Systems , 1999, IJCAI.

[2]  Peter van Beek,et al.  FROM PLAN CRITIQUING TO CLARIFICATION DIALOGUE FOR COOPERATIVE RESPONSE GENERATION * , 1993, Comput. Intell..

[3]  Robin Cohen,et al.  System initiative influenced by underlying representations in mixed-initiative planning systems , 2000 .

[4]  Mathias Bauer,et al.  Instructible information agents for Web mining , 2000, IUI '00.

[5]  Marilyn A. Walker,et al.  Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email , 1998, COLING-ACL.

[6]  Karen L. Myers.  Towards a Framework for Continuous Planning and Execution , 2000 .

[7]  Drew McDermott,et al.  Issues in the Development of Human-Computer Mixed Initiative Planning , 1994 .

[8]  Jennifer Chu-Carroll,et al.  Constructing and Utilizing a Model of User Preferences in Collaborative Consultation Dialogues , 1999, Comput. Intell..

[9]  Marilyn A. Walker,et al.  Evaluating Response Strategies in a Web-Based Spoken Dialogue Agent , 1998, ACL.

[10]  Anthony Jameson,et al.  When policies are better than plans: decision-theoretic planning of recommendation sequences , 2001, IUI '01.

[11]  Robin Cohen,et al.  User modeling in the design of interactive interface agents , 1999 .

[12]  Eric Horvitz,et al.  Principles of mixed-initiative user interfaces , 1999, CHI '99.

[13]  K..  Tracking Initiative in Collaborative Dialogue Interactions , 2002 .

[14]  Editors , 2003 .

[15]  Alfred Kobsa,et al.  Computational Models of Mixed-Initiative Interaction , 2007, Springer Netherlands.

[16]  Shimei Pan,et al.  Empirically Evaluating an Adaptable Spoken Dialogue System , 1999, ArXiv.

[17]  Cécile Paris,et al.  The role of the user's domain knowledge in generation , 1991, Comput. Intell..

[18]  Sarit Kraus,et al.  Intention reconciliation by collaborative agents , 2000, Proceedings Fourth International Conference on MultiAgent Systems.

[19]  Manuela M. Veloso,et al.  Controlling for Unexpected Goals when Planning in a Mixed-Initiative Setting , 1997, EPIA.