Multi-layer logic — A predicate logic including data structure as knowledge representation language
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A new generation computer is expected to be the knowledge processing system of the future. However, many aspects are yet unknown regarding this technology, and a number of fundamental concepts, directly concerning knowledge processing system design need investigation, such as knowledge, data, inference, communication, information management, learning, and human interface.
These concepts are closely related to knowledge representation. In particular, methodology to materialize such concepts as above in computers are completely dependent upon them. Thus, knowledge representation is a key concept in the design of knowledge processing systems and, consequently, of new generation computer systems.
Knowledge representation design is a very important task affecting the performance of new generation computer systems to be developed. We should first investigate the requirements for precise knowledge representation, considering its effects on system performance, then design knowledge representations to satisfy these requirements.
This paper discusses (1) a new style of information processing, (2) requirements for knowledge representation and (3) a knowledge representation satisfying these requirements, a knowledge processing system designed on this basis and a new style of problem solving using this system.
KeywordsKnowledge Representation Predicate Logic Multi-Layer Logic Data Structure Conceptual Modeling Hierarchical Abstraction
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- 1).Barr, A. and Feigenbaum, E. A. (eds.),The Handbook of Artificial Intelligence, Vol. 2, William Kaufmann, 1982.Google Scholar
- 2).Berge, C.,Graphs and Hypergraphs, North-Holland, 1973.Google Scholar
- 3).Bezier, P., “Mathematical and Practical Possibilities of UNISURF,” inComputer Aided Geometrical Design (R. E. Barnhill and R. F. Riesenfeld, eds.), Academic Press, 1974.Google Scholar
- 4).Brodie, M. L., Mylopoulos, J. and Schmidt, J. W. (eds.),On Conceptual Modelling — Perspectives from Artificial Intelligence, Databases and Programming Languages, Springer-Verlag, 1984.Google Scholar
- 5).Bubenko, J. A., “Information and Data Modelling: State of the Art and Research Directions,”Proc. Second Scandinavian Research Seminar on Information Modelling and Data Base Management (H. Kangassalo, ed.): also inActa Universitatis Tamperensis, Ser. B, Vol. 19, pp. 9–28, 1983.Google Scholar
- 6).Chang, C. L. and Lee, R. C. T.,Symbolic Logic and Mechanical Theorem Proving, Academic Press, 1972.Google Scholar
- 8).Date, C. J.,An Introduction to Data Base System, Addison-Wesley, 1976.Google Scholar
- 9).Davis, R. and Lenat, D.Knowledge Based Systems in Artificial Intelligence, McGraw-Hill, 1982.Google Scholar
- 10).Encarnacao, J. and Krause, F. L. (eds.),File Structures and Data Bases for CAD, North-Holland, 1982.Google Scholar
- 11).Enderton, H. B.,Mathematical Introduction to Logic, Academic Press, 1972.Google Scholar
- 12).Gallaire, H. and Minker, J. (eds.),Logic and Databases, Plenum Pub. Co., 1978.Google Scholar
- 13).Gallaire, H., Minker, J. and Nicolas, J. M. (eds.),Advances in Data Base Theory, Vol. 1, Plenum Pub. Co., 1981.Google Scholar
- 14).Gallaire, H., Minker, J. and Nicolas, J. M. (eds.),Advances in Data Base Theory, Vol. 2, Plenum Pub. Co., 1981.Google Scholar
- 15).Gallaire, H., Minker, J. and Nicolas, J. M., “Logic and Databased; A Deductive Approach,”Computing Surveys, Vol. 16, 1984.Google Scholar
- 16).Goldberg, A. and Robson, D.,Smalltalk-80, The Language and its Implementation, Addison-Wesley Pub. Co., 1983.Google Scholar
- 18).McLeod, D., “Abstraction in Database,”Proc. of ACM Workshop on Data Abstraction, Database and Conceptual Modelling pp. 19–25, June, 1980.Google Scholar
- 19).Ohsuga, S., “Perspectives on New Computer Systems of the Next Generation — A Proposal for Knowledge-Based Systems,”J. of Information Processing, No. 3, pp. 171–185, 1980.Google Scholar
- 20).Ohsuga, S., “A New Method of Model Description — Use of Knowledge Base and Inference,” inCAD System Framework (K. Bo and F. M. Lillehagen, eds.), North-Holland, pp. 285–312, 1983.Google Scholar
- 21).Ohsuga, S., “Predicate Logic Involving Data Structure as a Knowledge Representation Language,”Proc. Eighth Int. Joint Conf. on Artificial Intelligence, pp. 391–394, 1983.Google Scholar
- 23).Ohsuga, S., “A View to Knowledge Information System Design,”Proc. Int. Conf. on Computers, Systems and Signal Processing, IEEE, Bangalore, India, 1984.Google Scholar
- 25).Ohsuga, S., “Conceptual Design of CAD Systems Involving Knowledge Bases,”IFIP WG 5.2 Workshop on Knowledge Engineering in Computer-Aided Design, Sept., 1984; also to appear inKnowledge Engineering in Computer-Aided Design (J. S. Gero, ed.), North-Holland, (1985).Google Scholar
- 26).Reitman, W. (ed.),Artificial Intelligence Application for Business, Ablex Pub., 1983.Google Scholar
- 27).Rich, E.,Artificial Intelligence, McGraw-Hill, 1983.Google Scholar
- 28).Schmidt, J. W. and Blodie, M. L. (eds.),Relational Database Systems, Springer-Verlag, 1983.Google Scholar
- 29).Stonebraker, M., Rubenstein, B. and Guttman, A., “Application of Abstract Data Types and Abstract Indices to CAD Data Bases,”Proc. Engineering Design Applications of ACM-IEEE Data Base Week, pp. 107–113, 1983.Google Scholar
- 30).Wang, P. C. C. (ed.),Advanced Engineering Data Handling, Kluwer Academic, 1984.Google Scholar
- 31).Weinreb, D. and Moon, D., “Flavors: Message Passing in the Lisp Machine”MIT AI Memo, No. 602, 1980.Google Scholar
- 32).Yamauchi, H. and Ohsuga, S., “KAUS as a Tool for Model Building and Evaluation,”Proc. 5th International Workshop on Expert Systems and Their Applications, Avignon, France, May, 1985.Google Scholar
- 33).Zilles, S. N., “Types, Algebras and Modelling,”Proc. of Workshop on Data Abstraction, Database and Conceptual Modelling, pp. 207–209, 1980.Google Scholar