The Fifth International Conference on Artificial Intelligence Planning and Scheduling

The invited speakers at the conference presented some of their latest research and ideas on intelligent planning and execution: Drew McDermott from Yale University gave the first talk, entitled “Bottom-Up Knowledge Representation,” and David Smith from In recent years, AI planning and scheduling has emerged as a technology critical to production management, space systems, the internet, and military applications. The Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS2000) was held on 14–17 April 2000 at Breckenridge, Colorado;1 it was colocated with the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR2000). This conference brought together researchers working in all aspects of problems in planning, scheduling, planning and learning, and plan execution for dealing with complex problems. The format of the conference included paper presentations, invited speakers, panel discussions, workshops, and a planning competition. The conference was cochaired by Steve Chien of the Jet Propulsion Laboratory (JPL) at the California Institute of Technology, Subbarao Kambhampati of Arizona State University, and Craig Knoblock of the University of Southern California Information Sciences Institute, with the proceedings published by AAAI Press (Chien, Kambhampati, and Knoblock 2000). The three workshops were “Analyzing and Exploiting Domain Knowledge for Efficient Planning,” chaired by Maria Fox from University of Durham; “DecisionTheoretic Planning,” chaired by Richard Goodwin from IBM’s T. J. Watson Research Center and Sven Koenig from Georgia Institute of Technology; and “Model-Theoretic Approaches to Planning” by Paolo Traverso from information describing the content of documents or the behavior of programs. Because the described objects need to be processed by a wide variety of programs, designed by many different parties, finding a representation system to describe any content seems to be a daunting challenge. This challenge is similar to the well-known problems in trying to find a “formal theory of everything.” This talk described a more modest bottom-up approach that involves incrementally building small-specialized knowledge representation frameworks for immediate payoffs and facilitates greater payoffs as these small frameworks are linked together. This approach succeeds even if the process never converges to a general-purpose representation. Making it work involves carefully defining notions of when a framework is strictly more expressive than another and what it means to translate expressions within and between frameworks.