Login
Computer Science @ Rochester
Thursday, April 30, 2009
2:00 PM
Computer Studies Bldg. Room 632
Ph.D. Thesis Proposal
Daphne Liu
University of Rochester
Integrating Intrinsic Motivation into an Explicitly Self-Aware Dialogue Agent
Most work in AI intelligent agents lacks a well-balanced integration of self-motivation into planning and reasoning. Planning and reasoning have traditionally been viewed as being aimed at achieving given user goals, without allowance for subjective evaluation of the results of choices made. Conversely, most self-motivated agents function according to a utility-maximizing policy mapping states of world to the best actions to take in those states. However, the learning of the policy and the policy itself do not involve reasoning about actions and the expected action consequences. Moreover, the states of these agents typically do not have a symbolic representation amenable to general inference mechanisms. We propose an explicitly self-aware and deliberately self-motivated dialogue agent that integrates intrinsic motivation into its planning and reasoning. Our agent is primarily driven by its desire to optimize its own cumulative utility. It thinks ahead, plans and reasons deliberately, and acts reflectively, by drawing on its knowledge and by evaluating its action choices using a projective lookahead and its own metrics of rewards/costs. Furthermore, our agent can be seen as an integration of the behavioral (purely opportunistic) agent paradigm and the planning-based (goal-directed) agent paradigm.