Computer Science Building, Room 151
Faculty Host: Shlomo Zilberstein
One of the central problems faced by autonomous agents is the selection of the action to do next. In AI, three approaches have been used to address this problem: the programming-based approach, where the agent controller is hardwired, the learning-based approach, where the controller is learned from experience, and the model-based approach, where the controller is derived from a model. Planning in AI is best conceived as the model-based approach to the action selection problem. The models represent the initial situation, the actions, the sensors, and the goals. The main challenge in planning is computational, as all the models, whether accommodating feedback and uncertainty or not, are intractable. Thus planners must automatically recognize and exploit the structure of the given problems.
In this talk, I will review the models considered in current planning research, the progress achieved in solving these models, and the ideas that have turned to be most useful. I will also discuss some of the problems that remain open, and the use of planners for behavior recognition as opposed to behavior generation.
A reception will be held at 3:40 PM in the atrium, outside the presentation room.