Abstract

As part of the DARPA-sponsored High Performance Knowledge Bases program, four organisations were set the challenge of solving a selection of knowledge-based planning problems in a particular domain, and then modifying their systems quickly to solve further problems in the same domain. The aim of the exercise was to test the claim that, with the latest AI technology, large knowledge bases can be built quickly and efficiently. The domain chosen was ‘workarounds’; that is, planning how a convoy of military vehicles can ‘work around’ (i.e. circumvent or overcome) obstacles in their path, such as blown bridges or minefields.

This paper describes the four approaches that were applied to solve this problem. These approaches differed in their approach to knowledge acquisition, in their ontology, and in their reasoning. All four approaches are described and compared against each other. The paper concludes by reporting the results of an evaluation that was carried out by the HPKB program to determine the capability of each of these approaches.

Author Keywords: Knowledge-based planning; Knowledge acquisition; Ontology; Knowledge based systems

Article Outline

1. Introduction

2. Challenge problems

3. The Workaround planning challenge problem

4. AIAI's approach: hierarchical task network planning within CYC

5. TFS/Cycorp's approach: re-using pre-defined ontology in a Lisp-based planner

6. ISI's approach: EXPECT and knowledge acquisition scripts

7. GMU's approach: collaborative apprenticeship multi-strategy learning

8. Challenge problem evaluation

9. Results

10. Strengths of each approach

11. Summary

Acknowledgements

References