Prolog/Rex - A Way to Extend Prolog for Better Knowledge Representation

Prolog/Rex represents a powerful amalgamation of the latest techniques for knowledge representation and processing, rich in semantic features that ease the difficult task of encoding heterogeneous knowledge of real-world applications. The Prolog/Rex concept mechanism lets a user represent domain entities in terms of their structural and behavioral properties, including multiple inheritance, arbitrary user-defined relations among entities, annotated values (demons), incomplete knowledge, etc. A flexible rule language helps the knowledge engineer capture human expertise and provide flexible control of the reasoning process. Additional Prolog/Rex strength that cannot be found in any other hybrid language made on top of Prolog is language level support for keeping many potentially contradictory solutions to a problem, allowing possible solutions and their implications to be automatically generated and completely explored before they are committed. The same mechanism is used to model time-states, which are useful in planning and scheduling applications of Prolog/Rex. >

[1]  Dennis Merritt,et al.  Building Expert Systems in Prolog , 1989, Springer Compass International.

[2]  Ira P. Goldstein,et al.  The FRL Manual , 1977 .

[3]  Johan de Kleer,et al.  Extending the ATMS , 1986, Artif. Intell..

[4]  Johan de Kleer,et al.  An Assumption-Based TMS , 1987, Artif. Intell..

[5]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[6]  Daniel G. Bobrow,et al.  On Overview of KRL, a Knowledge Representation Language , 1976, Cogn. Sci..

[7]  Jon Doyle,et al.  A Truth Maintenance System , 1979, Artif. Intell..

[8]  Keith Weiskamp,et al.  Artificial intelligence programming with Turbo Prolog , 1988 .

[9]  Toramatsu Shintani,et al.  KORE: A Hybrid Knowledge Programming Environment for Decision Support Based on a Logic Programming Language , 1986, LP.

[10]  Richard Fikes,et al.  The role of frame-based representation in reasoning , 1985, CACM.

[11]  Charles L. Forgy,et al.  Rete: A Fast Algorithm for the Many Patterns/Many Objects Match Problem , 1982, Artif. Intell..

[12]  Ronald J. Brachman,et al.  An overview of the KL-ONE Knowledge Representation System , 1985 .

[13]  Mehmet Dincbas,et al.  Metacontrol of Logic Programs in Metalog , 1984, FGCS.

[14]  Robert E. Filman,et al.  Reasoning with worlds and truth maintenance in a knowledge-based programming environment , 1988, CACM.

[15]  Toramatsu Shintani,et al.  A Fast Prolog-Based Production System KORE/IE , 1988, ICLP/SLP.

[16]  Ivan Bratko,et al.  Prolog Programming for Artificial Intelligence , 1986 .

[17]  Neil C. Rowe Artificial intelligence through Prolog , 1988 .

[18]  Johan de Kleer,et al.  Problem Solving with the ATMS , 1986, Artif. Intell..

[19]  J. A. Rose Artifical Intelligence Through Prolog by Neils Rowe Prentice-Hall, Englewood Cliffs, New Jersey, U.S.A., 1988 (£15.95) , 1988, Robotica.

[20]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[21]  Marc B. Vilain,et al.  The Restricted Language Architecture of a Hybrid Representation System , 1985, IJCAI.