Selected Publications

Preference Learning

Personal Time Management

• Berry, P., Donneau-Golencer, T., Duong, K., Gervasio, M., Peintner, B. & Yorke-Smith, N.  (2008).  Emma: an event management assistant.  18th International Conference on Automated Planning and Scheduling (ICAPS-2008), Systems Demonstrations.  Sydney, Australia.
• Berry, P., Gervasio, M., Peintner, B. & Yorke-Smith, N.  (2007).  A preference model for over-constrained meeting requests.  Proceedings of the AAAI 2007 Workshop on Preference Handling for Artificial Intelligence.  Vancouver, Canada.
  
• Myers, K., Berry, P., Blythe, J., Conley, K., Gervasio, M., McGuinness, D., Morley, D., Pfeffer, A., Pollack, M. & Tambe, M.  An intelligent personal assistant for task and time management.  AI Magazine, 28(2), 47-61.
  
• Berry, P., Gervasio, M., Peintner, B. & Yorke-Smith, N.  (2007).  Balancing the needs of personalization and reasoning in a user-centric scheduling assistant.   Technical Note 561.  Menlo Park, CA: Artificial Intelligence Center, SRI International.
  
• Berry, P., Conley, K., Gervasio, M., Peintner, B., Uribe, T. & Yorke-Smith, N. (2006). Deploying a personalized time management agent.  Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-06), Industrial Track.  Hakodate, Japan.
• Berry, P., Gervasio, M., Peintner, B., Uribe, T. & Yorke-Smith, N. (2006). Multi-criteria evaluation in user-centric distributed scheduling agents. Proceedings of the AAAI Spring Symposium on Distributed Plan and Schedule Management.  Stanford, CA.
• Berry, P., Gervasio, M., Peintner, B., Uribe, T. & Yorke-Smith, N. (2005). Mixed-Initiative Issues for a Personalized Time Management Assistant. Proceedings of ICAPS’05 Workshop on Mixed-Initiative Planning and Scheduling (pp. 12-17). Monterey, CA.
• Gervasio, M. T., Moffitt, M. D., Pollack, M. E., Taylor, J. M., & Uribe, T. E. (2005). Active preference learning for personalized calendar scheduling assistance. Proceedings of the 2005 International Conference on Intelligent User Interfaces. San Diego, CA.
• Berry, P., Gervasio, M., Uribe, T., Myers, K. & Nitz, K. (2004).  A personalized calendar assistant.  Proceedings of the AAAI Spring Symposium on Interaction between Humans and Autonomous Systems over Extended Operation. Stanford, CA.

Crisis Management

• 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. Bled, Slovenia.
• Iba, W. & Gervasio, M. (1999). Adapting to user preferences in crisis response. Proceedings of the 1999 Conference on Intelligent User Interfaces. Redondo Beach, CA.
• Iba, W., Gervasio, M., Langley, P., & Sage, S. (1998). Evaluating computational assistance for crisis response. Proceedings of the Twentieth Annual Meeting of the Cognitive Science Society. Madison, WI.
• Gervasio, M. T., Iba, W., & Langley, P. (1998). Learning to Predict User Operations for Adaptive Scheduling. Proceedings of the Fifteenth National Conference on Artificial Intelligence (pp. 721-726). Madison, WI.
• Gervasio, M. T., Iba, W., & Langley, P. (1998). Case-based seeding for an interactive crisis response assistant. Case-Based Reasoning Integrations: Papers from the 1998 Workshop (Technical Report WS-98-15) (pp. 61-66) AAAI Press, American Association for Artificial Intelligence. Madison, WI,
• Gervasio, M., Iba, W., Langley, P., & Sage, S. (1998). Interactive adaptation for crisis response. Working Notes of the AIPS-98 Workshop on Interactive and Collaborative Planning (pp. 29-36). Pittsburgh, PA.
• Gervasio, M. T. (1998). Adaptive mixed-initiative systems for decision-theoretic crisis response. Interactive and Mixed-Initiative Decision Theoretic Systems: Papers from the 1998 AAAI Spring Symposium (Technical Report SS-98-03) (pp. 53-54). AAAI Press, American Association for Artificial Intelligence. Stanford, CA.
• Gervasio, M. T. & Iba, W. (1997). Crisis response planning: a task analysis (extended abstract). Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society (p. 929). Stanford, CA.

Stock Tracking

• Yoo, J., Gervasio, M., & Langley, P. (2003). An adaptive stock tracker for personalized trading advice. Proceedings of the 2003 International Conference on Intelligent User Interfaces (pp. 197-203). Miami, FL.

Learning for Planning and Execution

Learning from demonstration

• Eker, S., Lee, T. J. & Gervasio, M. (2009).  Iteration learning by demonstration.  Papers from the 2009 AAAI Spring Symposium on Agents that Learn from Human Teachers (forthcoming).
  
• Gervasio, M. T. & Murdock, J. L. (2009).  What were you thinking? Filling in missing dataflow through inference in learning from demonstration.  Proceedings of the 2009 International Conference on Intelligence User Interfaces.  Sanibel Island, FL.
  
• Spaulding, A., Blythe, J., Haines, W. & Gervasio, M. (2009).  From geek to sleek: integrating task learning tools to support end users in real-world applications.  Proceedings of the 2009 International Conference on Intelligence User Interfaces.  Sanibel Island, FL.
• Gervasio, M., Lee, T. J., & Eker, S. (2008). Learning email procedures for the desktop. Papers from the 2008 AAAI Workshop on Enhanced Messaging (Technical Report WS-08-04) (pp. 22-27).AAAI Press, American Association for Artificial Intelligence. Chicago, IL.
  

Mixed-initiative learning

• Gervasio, M., Myers, K., desJardins, M. & Yaman, F. (2009).  Question asking to inform preference learning: a case study.  Papers from the 2009 AAAI Spring Symposium on Agents that Learn from Human Teachers: (forthcoming).
• Gervasio, M., & Myers, K. (2008). Question asking to inform procedure learning. Papers from the 2008 AAAI Workshop on Metareasoning: Thinking about Thinking. (Technical Report WS-08-07) (pp. 68-75).  AAAI Press, American Association for Artificial Intelligence. Chicago, IL.

Learning conditionals

• Gervasio, M. T. (1996). An incremental curative learning approach for planning with incorrect domain theories. Ph.D. Dissertation, University of Illinois at Urbana-Champaign, 1996. (Also appears as Technical Report UIUCDCS-R-96-1972 (UILU-ENG-96-1733), Department of Computer Science, University of Illinois at Urbana-Champaign.) [gzip'd file]
  
• Gervasio, M. T. & DeJong, G. F. (1996), " Completable planning: a curative learning approach to the imperfect theory problem. Working Notes of the 1996 American Association for Artificial Intelligence Spring Symposium on Planning with Incomplete Information for Robot Problems (pp. 13-16). Palo Alto, CA
• Gervasio, M. T. & Dejong, G. F. DeJong (1994). An incremental learning approach to completable planning. Proceedings of the Eleventh International Conference on Machine Learning. New Brunswick, NJ.
  
• Gervasio, M. T. & Dejong, G. F. (1992), A completable approach to integrating planning and scheduling. Proceedings of the First International Conference on Artificial Intelligence Planning Systems. College Park, MD.
• Gervasio, M. & DeJong, G. (1992). Completable scheduling: an integrated approach to planning and scheduling. Working Notes of the 1992 American Association for Artificial Intelligence Spring Symposium on Practical Approaches to Scheduling and Planning (pp. 122-126). Drummond, M., Fox., M., Tate, A., & Zweben, M. (eds.). NASA Ames Research Center.

Learning achievable plans

• Gervasio, M. & Dejong, G. (1992). Goal-oriented sensing in completable planning. Working Notes of the 1992 American Association for Artificial Intelligence Spring Symposium on Selective Perception. Palo Alto, CA.
• Gervasio, M. T. (1990). Using qualitative reasoning in proving achievability. Proceedings of the 29th Institute of Electrical and Electronics Engineers Conference on Decision and Control. Honolulu, Hawaii.
• Gervasio, M. T. (1990). Learning general completable reactive plans. Proceedings of the Eighth National Conference on Artificial Intelligence (pp.1016-1021). Boston, MA.
  
• Gervasio, M. T. & Dejong, G. F. (1989). Explanation-based learning of reactive operators. Proceedings of the Sixth International Workshop on Machine Learning (pp. 252-254). Ithaca, NY.