Machine-Learning Challenges in Acting and Planning for Hierarchically Organized Systems
Abstract: To plan and carry out the activities of a hierarchically organized autonomous system, generally the planning and acting functions need to reflect the system's hierarchical organization. One approach for this is refinement planning and acting, in which tasks at each level of the hierarchy are refined into collections of tasks at the next level down, with the bottom levels consisting of "primitive" tasks (e.g., motion planning) to be sent to the system's execution platform(s). The speaker will give an overview of refinement planning and some of the challenges that it poses for machine learning.
Dana Nau is an AI researcher whose research includes both automated planning and game theory. Some of his best-known work includes the discovery of game trees that are "pathological" in the sense that deeper lookahead produces worse decisions; the strategic planning algorithm used to win the 1997 world championship of computer bridge; the SHOP and SHOP2 planning algorithms; two graduate-level textbooks on automated planning and acting; and evolutionary game-theoretic studies of the evolution of human behavioral norms. Dr. Nau has more than 300 refereed publications. He is an AAAI Fellow and an ACM Fellow.