Mixed-initiative Interaction

Mixed-Initiative Dialog in Collaborative Discourse

  Spoken Dialog Systems
  Virtual Humans and Collaborative Agents
  Mixed-Initiative Interaction
  Natural Language Processing

Papers by Curry Guinn

  • Efficient Collaborative Discourse: A Theory and its Implementation, with Alan Biermann, D. Richard Hipp, and Ronnie Smith in ARPA Workshop on Human Language Technology, Princeton, NJ, March 1993. (pdf) .


    An architecture for voice dialogue machines is described with emphasis on the problem solving and high level decision making mechanisms. The architecture provides facilities for generating voice interactions aimed at cooperative human-machine problem solving. It assumes that the dialogue will consist of a series of local self-consistent subdialogues each aimed at subgoals related to the overall task. The discourse may consist of a set of such subdialogues with jumps from one subdialogue to the other in a search for a successful conclusion. The architecture maintains a user model to assure that interactions properly account for the level of competence of the user, and it includes an ability for the machine to take the initiative or yield the initiative to the user. It uses expectation from the dialogue processor to aid in the correction of errors from the speech recognizer.

  • A Computational Model of Dialogue Initiative in Collaborative Discourse, in Human-Computer Collaboration: Reconciling Theory, Synthesizing Practice, Technical Report FS-93-05, The AAAI Press, 1993. (pdf) .


    This paper presents a model of dialogue initiative within a collaborative discourse. The importance of varying initiative between participants is validated both in theory and in experiments. For example, it was found that one initiative setting algorithm was up to 50%better than if initiative is random. Furthermore, a method for varying levels of initiative between participants is presented which relies on a relatively simple user model.

  • Maximally Efficient Dialogue Mode Algorithm, in Knowledge-Based Systems, 7(4):277-8, December, 1994.

  • Mechanisms for Mixed-Initiative Human-Computer Collaborative Discourse, in Proceedings of the 34th Annual Meeting of the Association for Computational Linguistics , 1996. (abstract) , (pdf) , (PostScript)


    In this paper, we examine mechanisms for automatic dialogue intiative setting. We show how to incorporate initiative changing in a task-oriented human-computer dialogue system, and we evaluate the effects of intiative both analytically and via computer-computer dialogue simulation.

  • Goal-Oriented Multimedia Dialogue with Variable Initiative, with Alan W. Biermann, Michael S. Fulkerson, Greg A. Keim, Zheng Liang, Douglas M. Melamed, Krishnan Rajagopalan, in International Symposium on Methodologies for Intelligent Systems, pp. 1-16, 1997.


    Tutorial dialogue offers several interesting challenges to mixed-initiative dialogue systems. In this paper, we outline some distinctions between tutorial dialogues and the more familiar task-oriented dialogues, and how these differences might impact our ideas of focus and initiative. In order to ground discussion, we describe our current dialogue system, the Duke Programming Tutor. Through this system, we present a temperature-based model and algorithm which provide a basis for making decisions about dialogue focus and initiative.

  • An Analysis of Initiative Selection in Collaborative Task-Oriented Discourse, User Modeling and User-adapted Interaction, Vol 8(3-4):255-314, 1998. Also published in Computational Models of Mixed-Initiative Interaction, editors, S. Haller, S. McRoy, and A. Kobsa, Kluwer Academic Publishers, pp. 89-148, 1999. (abstract) , (postscript) , (pdf)


    In this paper we propose a number of principles and conjectures for mixed-initiative collaborative dialogs. We explore some methodologies for managing initiative between conversational participants. We mathematically analyze specific initiative-changing mechanisms based on a probabilistic knowledge base and user model. We look at the role of negotiation in managing initiative quantify how the negotiation process is useful toward modifying user models. Some experimental results using computer--computer simulations are presented along with some discussion of how such studies are useful toward building human--computer systems.

  • Evaluating Mixed-Initiative Dialog, IEEE Intelligent Systems , Volume 14, Number 5, pp. 21-23, 1999. (abstract) , (pdf)


    Researchers in mixed-initiative interaction are trying to make computers be collaborators with their human users. In this two-way information exchange, the computer can do some tasks better alone, some tasks require joint work, and some tasks are better done by the human user. The challenge is to define computation models of how initiative is or should be controlled in a dialog.
    In current user-interface design, the pre-dominate initiative structure has the human user initiating almost every interaction. Only in some very fixed, a priori-designed instances might the computer initiate interactions ("All files in directory will bedeleted! Are you sure (Y/N)?"). In early human-computer interfaces, these designer-selected interfaces were often faulted for being too rigid. So, today, almost all complex software comes with a multitude of user-preference selections.
    In moving away from these rigid paradigms of interaction, we strive toward the more loose, open, and dynamic interaction patterns seen in human-human conversations. Flow control there is often very fast-paced, with many dialog turns and shifts in initiative.
    In this essay, I will focus on how to evaluate and compare computational models of mixed-initiative dialog. I will focus on two aspects of this evaluation:
    What are the metrics used for evaluating dialog systems?
    What is the nature of the data set being used for evaluation?