This Article 
 Bibliographic References 
 Add to: 
Prolog/Rex-A Way to Extend Prolog for Better Knowledge Representation
February 1994 (vol. 6 no. 1)
pp. 22-37

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] J. S. Aikins, "A representation scheme using both frames and rules" inRule Based Expert Systems, B. G. Buchanan and E. Shortlife, Eds. Reading, MA: Addison-Wesley, 1984, pp. 424-440.
[2] D. G. Bobrow and T. Winograd, "An overview of KRL-O, a knowledge representation language,"Cognitive Science 1, 1977.
[3] R. J. Brachman and J. G. Schmolze, "An overview of the KL-ONE knowledge representation system,"Cognitive Science 9, 1985, pp. 171-216.
[4] I. Bratko,PROLOG: Programming for Artificial Intelligence. Reading, MA: Addison-Wesley, 1986.
[5] B. G. Buchanan, E. H. Shortlife,Rule-Based Expert Systems. Reading, MA: Addison-Wesley, 1985.
[6] W. F. Clocksin and C. S. Mellish,Programming in Prolog. New York: Springer-Verlag, 1984.
[7] L. Console and G. Rossi, "Using prolog for building FROG, a hybrid knowledge representation system,"New Generation Computing, no. 6, 361-388, 1989.
[8] J. de Kleer, "An assumption-based TMS,"Artificial Intell., vol. 28, pp. 127-162, 1986.
[9] J. De Kleer, "Extending the ATMS,"Artificial Intell., vol. 28, pp. 163-196, 1986.
[10] J. De Kleer, "Problem solving with the ATMS,"Artif. Intell., vol 28, no. 2, Mar. 1986.
[11] P. Devanbu, M. Freeland, and S. Naqvi, "A procedural approach to search control in prolog," inProc. Int. Conf. ECAI'86a.
[12] M. Dincbas and J-P. La Pape, "Metacontrol of logic programs in METALOG," inProc. Int. Conf. 5th Generation Computer Systems, 1984.
[13] J. Doyle, "A truth maintenance system,"Artificial Intell., vol. 12, pp. 231-272, 1979.
[14] R. Fikes and T. Kehler, "The role of frame-based representation in reasoning,"Commun. ACM, vol. 28, pp. 904-920, Sept. 1985.
[15] R. Filman, "Reasoning with worlds and truth maintenance in a knowledge-based system shell," IntelliCorp, 1986.
[16] C. L. Forgy, "RETE--a fast algorithm for the many pattern/many object pattern match problem,"Artificial Intell., vol. 12, pp. 17-37, 1980.
[17] H. Gallaire and C. Lasserre, "Metalevel control for logic programs," inLogic Programming, K. Clark and Tarnlund, Eds. San Diego: Academic, 1982.
[18] P. Harmon and D. King,Expert Systems. New York: Wiley, 1985.
[19] ART-Automated Reasoning Tool User's Manual, version 3.0, Inference Corporation, Los Angeles, CA, 1987.
[20] KEE Software Development System User's Manual, IntelliCorp Inc., Mountain View, CA, 1986.
[21] IF/Prolog Reference Manual, version 3.4, InterFace Compute GmbH, Munich, Germany, 1988.
[22] D. Merrit,Building Expert Systems in Prolog. New York: Springer, 1989.
[23] M. Minski, "A framework for representing knowledge." inThe Psychology of Computer Vision, P. Winston, Ed. New York: McGraw-Hill, 1975.
[24] R. B. Roberts and I. P. Goldstein, "The FRL manual," MIT, AI Memo 409, Sept. 1977.
[25] N. C. Rowe,Artificial Intelligence through Prolog. Englewood Cliffs, NJ: Prentice, 1988.
[26] T. Shintani, "A fast prolog based production system KORE/IE," inProc. 5th Conf. and Symp. in Logic Programming, Tokyo, June 23-26, 1986.
[27] T. Shintani, Y. Katayama, K. Hiraishi, and M. Toda, "KORE: A hybrid knowledge programming environment for decision support based on a logic programming language," inProc. 5th Conf. and Symp. in Logic Programming, Tokyo, June 23-26, 1986.
[28] M. Vilain, "The restricted language architecture of a hybrid representation system," inProc. Int. Joint Conf. Artificial Intelligence, Los Altos, CA, 1985, pp. 547-551.
[29] S. Vranes, M. Stanojevic´, "Prolog/Rex inference engine," submitted for publication.
[30] K. Weiskamp, T. Hengl,Artificial Intelligence Programming with Turbo Prolog. New York: Wiley, 1988.

Index Terms:
PROLOG; knowledge representation; expert systems; logic programming; logic programming languages; Prolog/Rex; Prolog; knowledge representation; knowledge processing; semantic features; heterogeneous knowledge; behavioral properties; structural properties; multiple inheritance; arbitrary user-defined relations; incomplete knowledge; flexible rule language; reasoning; knowledge engineer; hybrid language; time-state modelling; planning; scheduling applications; expert systems; artificial intelligence; logic programming
S. Vranes, M. Stanojevic, "Prolog/Rex-A Way to Extend Prolog for Better Knowledge Representation," IEEE Transactions on Knowledge and Data Engineering, vol. 6, no. 1, pp. 22-37, Feb. 1994, doi:10.1109/69.273023
Usage of this product signifies your acceptance of the Terms of Use.