Advanced Planning Technology: Technological Achievements of the ARPA/Rome Laboratory Planning Initiative
Knowledge engineering is a critical task in the development of AI planning applications. In order to build large-scale, real-world planning applications, tools must be developed that will provide efficient, effective ways to create, modify, debug, and extend the knowledge bases for such systems. As much as possible, this development and updating process should be automated. The goal of this project is to develop knowledge acquisition tools for AI planning systems. We have developed a graphical Operator Editor, which allows users to develop new planning operators and revise existing operators, and the Operator Learner, an inductive learning-based tool for knowledge engineering. Both tools have been implemented within the SOCAP planning system. This paper describes these two tools, discusses related work, and summarizes directions for future research.