Knowledge Acquisition for Knowledge-based Systems: Proceedings of the Japanese Workshop, JKAW '90

The knowledge acquisition bottleneck has become a major impediment to the development and application of effective knowledge-based systems. Many research groups around the world have been working on KA methodologies, techniques and tools to overcome this problem. In this volume, two tutorial surveys give an introduction to the three generations of KA techniques, culminating in the development of tools to provide integrated KA environments. Methods for acquiring and modelling knowledge are analyzed, as automated methods are expected to be subject to extensive research during the coming decade. In order to make the next generation expert systems more useful and practical, progress towards overcoming the KA bottleneck is regarded as necessary. Researchers and students of computer science and artificial intelligence may find this volume a useful introduction to, and overview of KA research.

Table of Contents:
Foundations of knowledge acquisition, B.Gaines and M.Shaw; knowledge acquisition tools, methods and mediating representations, J.Boose; implications of the system-model-operator metaphor for knowledge acquisition, W.Clancey; an architecture of knowledge acquisition by interview based on dynamic analysis, A.Kawaguchi et al; inductive knowledge acquisition from structured data, J.Quinlan; research activities of knowledge acquisition and learning in Japan, S.Kobayashi et al; explorations on how to make application programming easier, J.McDermott et al; justification-based knowledge acquisition, T.Gruber; AKARS-1 - an automatic knowledge acquisition and refinement system, K.Leung and M.Wong; a knowledge acquisition system that aims at integrating inductive learning and explanation-based reasoning, K.Tsujino et al; novice - getting textbook knowledge into expert systems, M.Medow and L.Travis; a new approach to classification knowledge refinement, K.Hagiwara; rule optimization and theory optimization - heuristic search strategies for data driven machine learning, G.Webb; a combined approach to text retrieval using concept networks linked to hypertext, P.Cunningham et al; knowledge acquisition with a special hypertext system, H.Langendorfer et al; knowledge acquisition from machine adjustment cases using causal model and operationality criteria, K.Nakamura and S.Kobayashi; ISAK (Interview System for Acquiring Design Knowledge) - a new architecture of interview systems using examples, R.Mizoguchi et al; assisting the articulation of the mental world, K.Hori and S.Ohsuga; automated versus manual knowledge acquisition - a comparison in a real domain, F.Bergadano et al; a proposal guided knowledge acquisition support system, H.Taki and S.Terasaki; a task analysis interview system that uses a problem-solving model, Y.Tijerino et al; hierarchical knowledge representation based on approximations, K.Yoshida and H.Motoda; a knowledge acquisition method for distributed information systems - a multi-database approach, A.Barfoursh and M.Rogers; knowledge acquisition in a development methodology for knowledge-based applications, S.Spirgi et al; a framework of learning systems RLS, M.Tajima; knowledge representation servers - a generic technology for knowledge acquisition and knowledge-based systems, B.Gaines; acquiring text editing programs from examples, D.Mo.