Learning and Adaptation for Crisis Response
By their very nature, crises place overwhelming demands on the human
planners who must cope with them. This research effort aims to develop
an intelligent advisory system that will assist humans in responding
to complex crises, focusing especially on the task of planning and
scheduling responses to chemical spills and fires.
The basic approach involves retrieving an appropriate plan from a
preexisting plan library, adapting this plan to the current situation,
and tracking it during implementation, adapting it further as the need
arises. The advisory nature of the system supports unobtrusive
collection of users' decisions, which in turn provides training data
Experimental studies have shown that using previous plans reduces user
response time while maintaining response quality and that, over time,
the advisory system can adapt its behavior to different users. A
longer-term goal of this subproject is to use learning methods to
acquire coordination strategies among different planners, and thus to
reduce the number of conflicts that arise during the crisis-planning
This work was funded by the Office of Naval Research through Grant
Contributors to the Project
Dr. Melinda Gervasio
Dr. Wayne Iba
Professor Pat Langley
Gervasio, M. T., Iba, W., & Langley, P. (1999).
Learning user evaluation functions for adaptive scheduling assistance.
Proceedings of the Sixteenth International Conference on Machine
Learning (pp. 152-161). Bled, Slovenia: Morgan Kaufmann.
Langley, P., & Fehling, M. (1998).
The experimental study of adaptive user interfaces (Technical Report
98-3). Institute for the Study of Learning and Expertise, Palo Alto, CA.
Iba, W., Gervasio, M., Langley, P., & Sage, S. (1998).
Evaluating computational assistance for crisis response.
Proceedings of the Twentieth Annual Conference of the Cognitive
Science Society. Madison, WI: Lawrence Erlbaum.
Gervasio, M., Iba, W., & Langley, P. (1998).
Learning to predict user operations for adaptive scheduling.
Proceedings of the Fifeenth National Conference on Artificial
Intelligence (pp. 721-726). Madison, WI: AAAI Press.
Gervasio, M., Iba, W., & Langley, P. (in press).
Case-based seeding for an interactive crisis response assistant.
Proceedings of the AAAI-98 Workshop on Case-Based Reasoning Integrations.
Gervasio, M., Iba, W., Langley, P., & Sage, S. (1998).
Interactive adaptation for crisis response.
Proceedings of the AIPS-98 Workshop on Interactive and Collaborative
Planning. Pittsburgh, PA.
Iba, W. and Gervasio, M. T. (1997).
Crisis response planning: A task analysis.
Unpublished manuscript, Institute for the Study of Learning and
Expertise, Palo Alto, CA.
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