A Call for Knowledge-Based Planning

We are interested in solving real-world planning problems and, to that end, argue for the use of domain knowledge in planning. We believe that the field must develop methods capable of using rich knowledge models to make planning tools useful for complex problems. We discuss the suitability of current planning paradigms for solving these problems. Real-world problems have been found to require more expressive representations and capabilities than are needed for the standard set of benchmark planning problems (blocks world, towers of Hanoi, simplified logistics, and the like) or for the problems used in the 1998 and 2000 Artificial Intelligence Planning and Scheduling (AIPS) Conference planning competitions (Bacchus et al. 2000; Long 2000; McDermott 2000). Past research in AI planning can roughly be divided into two camps: (1) systems that take a minimalist approach to domain knowledge and (2) systems that focus on leveraging as much domain knowledge as possible.