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First, t h e DesiredDuration is found based on 46 JOURDAN, D E N T , M C D E R M O T T , MITCHELL, AND ZABOWSKI Figure 2-4. Dependency of meeting duration on other knowledge, static features of the meeting. This value refers t o the duration t h a t would be preferred if there were no conflicting demands on the participants' time. Second, the current calendar is checked to see how busy the calendar's owner is. If the owner is very busy, the final computed Duration will be shorter t h a n the DesiredDuration; otherwise, t h e Duration is simply equal to the DesiredDuration.
4 L e a r n i n g t o R e a c t v e r s u s L e a r n i n g t o P l a n In recent years, reactive behavior has become a very active topic in the planning and robotics communities. In reactive systems, some of t h e responsibility for decision making is placed on an execution subsystem LEARNING, PLANNING AND SCHEDULING 23 rather t h a n relying solely on the plan generation subsystem. T h e development of reactive systems naturally raises the question of how to learn reactive strategies (Bresina, Drummond, & Kedar, this volume).
AAAI. Los Altos, CA: Morgan Kaufmann (distributor). , h Likuski, J. (1989). Incorporating redundant learned rules: A preliminary formal analysis of EBL. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 7 4 4 749). AAAI. San Mateo, CA: Morgan Kaufmann (distributor). Gupta, A. (1987). Explanation-based failure recovery. Proceedings of the Sixth National Conference on Artificial Intelligence (pp. 600-605). AAAI. Los Altos, CA: Morgan Kaufmann (distributor).