Although planning is one of the oldest research areas of AI, recent years have brought many dramatic advances in both its theory and practice. On the theory side, we now understand the deep connections among AI planning, constraint satisfaction, logic and operations research. On the practical side, we have effective ways of capturing and using domain-specific control knowledge, and have planners that are capable of synthesizing plans with hundred or more actions in minutes. These, in short, are exciting times for AI planning research.
This tutorial will provide a comprehensive overview of the field, placing both the traditional ideas and the recent advances in a unified perspective, and delineating their application potential. Our primary emphasis will be on planning in deterministic domains, although we shall make several connections to scheduling as well as planning in stochastic domains.
The tutorial should be accessible to anyone with basic computer science and AI background.
Subbarao Kambhampati is an associate professor of computer science at Arizona State University, where he directs the YOCHAN research group. He received his bachelors degree in electrical engineering from Indian Institute of Technology, Madras, and M.S. and Ph.D. degrees in Computer Science from University of Maryland, College Park. He has published over seventy technical articles on planning, learning and related areas of AI. He was a 1994 NSF Young Investigator and a 1996 AAAI invited speaker. He has taught courses and has published several tutorial articles on AI planning, and is the author of a 1997 IJCAI challenge paper on bridging plan-synthesis paradigms.