Meeting time: Monday 7:20pm – 10pm
Meeting location: ST-II, Room 430A

Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science
Office hours: Monday, 6pm – 7pm
Office: ST-II, Rm. 421
Phone: 993-1722
E-mail: tecuci@gmu.edu

For current information on this course see:
http://lalab.gmu.edu/it803-sp04/it803-sp04.htm

Prerequisite: an introductory course in artificial intelligence.

Mixed-initiative intelligent systems integrate human and automated reasoning to take advantage of their complementary knowledge, reasoning styles and computational strengths. These systems may include several automated agents that exhibit mixed-initiative behavior when collaborating with one another.

In this course, the students will learn about the open research issues in the development of such systems, including:

· The task issue: the division of responsibility between the human and the agent(s) for the tasks that need to be performed.

· The control issue: the shift of initiative and control between the human and the agent(s), including proactive behavior.

· The awareness issue: the maintenance of a shared awareness with respect to the current state of the human and agent(s) involved.

· The communication issue: the protocols that facilitate the exchange of knowledge and information between the human and the agent(s), including mixed-initiative dialog and multi-modal interfaces.

· The evaluation issue: the human and automated agent(s) contribution to the emergent behavior of the system, and the overall system's performance (e.g., versus fully automated, fully manual, or alternative mixed-initiative approaches).

· The architecture issue: the design principles, methodologies and technologies for different types of mixed-initiative roles and behaviors.

These issues will be discussed in the context of current research on:

· Mixed-initiative development of intelligent systems
(e.g. knowledge engineering, knowledge acquisition, teaching and learning)

· Specific mixed-initiative intelligent systems
(e.g., planning systems, dialog systems, discovery systems, learning systems, design systems, tutoring systems)

· Mixed-initiative maintenance of intelligent systems
(including knowledge base refinement and optimization)

· Knowledge representation for mixed-initiative reasoning
(e.g., ontologies and other shared representations suitable for both human and agents)

This course is intended to help the students make progress with their own dissertation research. The students will study several state of the art papers in mixed-initiative reasoning, related to their own research interests. These papers will be analyzed from the point of view of the above research topics (i.e. task, control, awareness, communication, evaluation, and architecture), and will be presented to the class. All the students are expected to actively participate in discussing these research issues, collaborating in identifying theoretical, methodological and practical foundations for mixed-initiative systems, based on all the analyzed papers. The course will also include brainstorming discussions on applying these concepts to practical systems of interest to the students.

Tecuci G., Aha D., Boicu M., Cox M., Ferguson G., and Tate A. (eds), Proceedings of the IJCAI Workshop on Mixed-Initiative Intelligent Systems, Acapulco, Mexico, AAAI Press, Menlo Park, CA, August 2003. http://lalab.gmu.edu/miis/proceedings.html

Aha D. et al. (eds.), Proceedings of the ECCBR Workshop on Mixed-Initiative Case-Based Reasoning, 4 September 2002, Aberdeen, Scotland, UK.

Cox M. et al., (eds), Proceedings of the AAAI Workshop on Mixed-Initiative Intelligence, July 18-19, Orlando, Florida, AAAI Press, Menlo Park, CA. 1999. http://www.cs.wright.edu/~mcox/mii/

User Modeling and User-Adapted Interaction, The Journal of Personalization Research, Vol. 8, no.3, 1998, and Vol. 9, no.1, 1999, special issues on mixed-initiative interaction, available in line at: http://www.kluweronline.com/issn/0924-1868

Other papers on state of the art mixed-initiative reasoning systems.