Towards Versatile and Practical Knowledge Acquisition

Abstract

Abstract : Rapid prototyping and tool reusability have pushed knowledge acquisition research to investigate method-specific knowledge acquisition (KA) tools appropriate for pre-determined problem-solving methods. We believe that method-dependent KA is not the only approach. The aim of our research is to develop powerful but versatile machine learning mechanisms that can be incorporated into general-purpose but practical knowledge acquisition tools. This paper shows through examples the practical advantages of this approach. In particular, we illustrate how existing knowledge can be used to facilitate KA through analogy mechanisms in a logistics transportation domain and use the same general-purpose mechanism for knowledge acquisition in a different domain through plan generalization. Our hypothetical knowledge acquisition dialogs with a domain expert illustrate which parts of the process are addressed by the human and which parts are automated by the tool, in a synergistic cooperation for knowledge-based extension and refinement. The paper also describes briefly the EXPECT problem-solving architecture and knowledge representation language that facilitate this approach to Knowledge acquisition.