The First Competition on Knowledge Engineering for Planning and Scheduling

tion, led to a rapid development of techniques, established a common language for communicating the dynamics of domain models (PDDL), and helped in the validation and scrutiny of planning algorithms. Further, the IPC has facilitated the sharing of benchmark domain models, tasks, and planning tools through the use of PDDL. However, the narrow focus and limiting assumptions of the IPC are controversial—it encourages rapid development, but in the narrow area of fully autonomous plan generation. Competitors taking part in the IPC act on the assumption that the input to their planning engine (a domain model and a planning task) constitutes a bug-free statement of the dynamics of a world that the model represents. The domain models and planning tasks have to be highly refined to ensure that solutions are logically possible. Further, they must be written to satisfy the limiting environmental assumptions of the class of planners that they are testing. In reality, autonomous plan generation is only one part of systems that ■ We report on the staging of the first competition on knowledge engineering for AI planning and scheduling systems, held in Monterey, California, in colocation with the ICAPS 2005 conference. The background and motivation is discussed, together with the relationship of this new competition with the current international planning competition. We report on the new competition’s format, its outcome, and the benefits we hope it will bring to the research area.