Advertisement

A conceptual and contextual object-oriented logic programming: The PROLOG++ language

  • Adil Kabbaj
  • Claude Frasson
  • Marc Kaltenbach
  • Jean-Yves Djamen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 835)

Abstract

Building upon J. Sowa's Conceptual Graph (CG) theory, this paper introduces basic elements of the new language, PROLOG++, subsuming Prolog with various objet oriented, conceptual and contextual extensions. A Prolog++ “program” is composed of a declarative knowledge base and a distributed strategic knowledge base; the latter forms a network of objects that communicate by sending messages. A message corresponds to a goal described by a term or a CG (simple or compound). Declarative knowledge base corresponds to a “conceptual dictionary” describing the semantic of concepts and relations used in CG. The declarative base is composed of two hierarchies, one for concepts and the other for relations, each element of the two hierarchies corresponds to an object made up of conceptual structures. This base thus endows Prolog++ with a second form of object oriented programming. Finally, Prolog++ provides, as predefined methods, a set of conceptual operations for editing and handling CG.

Key words

Prolog extension CG theory object oriented programming conceptual programming contextual programming high-order logic programming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aït-Kaci H. and R. Nasr R. (1986), LOGIN: A logic programming language with built-in inheritance, Journal Logic Programming, 3, pp. 185–215.Google Scholar
  2. 2.
    Aït-Kaci H. and A. Podelski, Logic programming with functions over order-sorted feature terms, in E. Lamma and P. Mello (Eds.), Extensions of Logic Programming, Springer-Verlag, pp. 100–119, 1992.Google Scholar
  3. 3.
    Bläsius K. H., Hedtstück U. and Rollinger C.-R., Eds., Sorts and Types in Artificial Intelligence, Lecture Notes In Artificial Intelligence, No. 418, Springer-Verlag, 1990.Google Scholar
  4. 4.
    Boksenbaum C., B. Carbonneill, O. Haemmerlé et T. Libourel, Conceptual graphs for relational databases, in G. W. Mineau, B. Moulin et J. F. Sowa (eds.), Conceptual Graphs for Knowledge Representation, Springer-Verlag, 1993.Google Scholar
  5. 5.
    Carpenter B., The logic of typed feature structures, Cambridge Univ. Press, 1992.Google Scholar
  6. 6.
    Chan M. C., B. J. Garner et E. Tsui, Recursive modal unification for reasoning with knowledge using a graph representation, Knowledge-Based Syst., 1:2, pp. 94–104, 1988.Google Scholar
  7. 7.
    Chosh B. C. and V. Wuwongse, Declarative Semantics of Conceptual Graph Programs, in R. Levinson et G. Ellis (eds), Proc. of the 2 Inter. Workshop on PEIRCE, 1993.Google Scholar
  8. 8.
    Clancey W. J., Knowledge-Based Tutoring: The GUIDON Program, MIT Press, 1987.Google Scholar
  9. 9.
    Denti E., E. Lamma, P. Mello, A. Natali and A. Omicini, Techniques for implementing Contexts in Logic Programming, in E. Lamma and P. Mello (Eds.), Extensions of Logic Programming, Springer-Verlag, pp. 100–119, 1992.Google Scholar
  10. 10.
    Dichev C., Distributed knowledge and data processing, in ICO'93 Proceeding, pp. 272–282, 1993.Google Scholar
  11. 11.
    Dichev C., Logic programming with worlds, in: Artificial Intelligence: Methodology, Systems, Applications, North-Holland, 1992, pp. 57–67.Google Scholar
  12. 12.
    Djamen J-Y, M. Kaltenbach and C. Frasson, The interactive planning with PIF, in ITS'92, 1992.Google Scholar
  13. 13.
    Ellis G. and R. Levinson (eds), Proc. of the 1 Inter. Workshop on PEIRCE: A Conceptual Graphs Workbench, 1993.Google Scholar
  14. 14.
    Ellis G., PEIRCE User Manual, 1993.Google Scholar
  15. 15.
    Fargues J., Landau M-C, Duguord A. and Catach L. (1986), Conceptual graphs for semantics and knowledge processing, IBM Journal of Research and Development, v. 30:1, pp. 70–79.Google Scholar
  16. 16.
    Fargues J., CG information retrieval using linear resolution, generalization and graph splitting, in the 4 Int. Workshop on CGS, 1989 (see also a chapter by Fargues in Nagle et al. (eds.), 1992).Google Scholar
  17. 17.
    Ferber J. and P. Volle, Introduction to an intensional theory of object knowledge representation, in E. Chouraqui (ed), Modélisation de la connaissance et du raisonnement, Compte-rendu des Journées d'études des 8 et 9 Février 1988.Google Scholar
  18. 18.
    Frasson C., G. Gauthier (eds.), Intelligent Tutoring Systems (ITS'92), Springer-Verlag, 1992.Google Scholar
  19. 19.
    Fukunaga K. and S. Hirose, An experience with a Prolog-based Object-Oriented Language, In OOPSLA'86 Proceedings, pp. 224–231, 1986.Google Scholar
  20. 20.
    Gabbay D. M. and U. Reyle, N-Prolog: an extension of prolog with hypothetical implications, J. Logic Programming, 2, pp. 251–284, 1985.Google Scholar
  21. 21.
    Garner B. J. and E. Tsui, An extendible graph processor for knowledge engineering, in J. F. Gilmore (ed), Applications of AI 3, 1986.Google Scholar
  22. 22.
    Garner B. J. and E. Tsui, General purpose inference engine for canonical graph models, Knowledge-Based Systems, 1:5, pp. 266–278, 1988.Google Scholar
  23. 23.
    Garner B. J., E. T. Tsui, D. Lui, D. Lukose and J. Koh, Extendible Graph Processing in Knowledge Acquisition, Planning and Reasoning, in Nagle et al. (eds.), 1992.Google Scholar
  24. 24.
    Herzog O. and C.-R. Rollinger (Eds.), Text Understanding in LILOG, Springer-Verlag, 1991.Google Scholar
  25. 25.
    Kabbaj A., SMGC: un système de manipulation des graphes conceptuels, M. Sc. Thesis, Dept. Informatique, Université Laval, 1987, Québec, Canada. Some part of the thesis appears in B. Moulin et A. Kabbaj, SMGC: A tool for conceptual graphs processing, in The Journal for the integrated study of artificial intelligence, cognitive science and applied epistemology, 7:1, pp. 23–47, 1990.Google Scholar
  26. 26.
    Kabbaj A., Le système de représentation et manipulation des connaissances: SRMC, Internal Report, DIRO, Université de Montréal, 1993.Google Scholar
  27. 27.
    Kabbaj A., Toward a conceptual actor language, in Mineau et al. (eds.), 1993.Google Scholar
  28. 28.
    Kabbaj A., Declarative programming in Prolog++, submitted to ALP-PLILP'94 Joint Conference, 1994.Google Scholar
  29. 29.
    Kabbaj A., Current practice and research in CAL, Internal Report, DIRO, Université de Montréal, 1994.Google Scholar
  30. 30.
    Kabbaj A. and C. Frasson, Acquisition des connaissances dans le système SRMC, in ACTI Conf., Limoges, 1993.Google Scholar
  31. 31.
    Kabbaj A. and C. Frasson, Toward a dynamic model of memory, in Mineau et al. (eds.), 1993.Google Scholar
  32. 32.
    Kabbaj A. and C. Frasson, A conceptual algebra for Prolog++, submitted to ALPPLELP'94 Joint Conference, 1994.Google Scholar
  33. 33.
    Kabbaj A. and C. Frasson, A new programming language: Prolog++, Internal Report, DIRO, Université de Montréal, 1994.Google Scholar
  34. 34.
    Kauffmann H. and A. Grumbach, MULTILOG: MULTIple worlds in LOGic programming, in the proceeding of the 7th European Conference on AI, 1986.Google Scholar
  35. 35.
    Kocura P. and K. Kwong Ho, Aspects of Conceptual Graphs Processor Design, in the 7 Int. Workshop on CGS, 1992.Google Scholar
  36. 36.
    Levinson R. and G. Ellis (eds), Proc. of the 2 Inter. Workshop on PEIRCE: A Conceptual Graphs Workbench, 1993.Google Scholar
  37. 37.
    McCabe F. G., L&O: Logic and Objects, Prentice-Hall, 1992.Google Scholar
  38. 38.
    Mineau G., B. Moulin and I. Sowa (eds.), Conceptual Graph for Knowledge Representation, Springer-Verlag, 1993.Google Scholar
  39. 39.
    Monteiro L. and A. Porto, Contextual Logic Programming, in G. Levi and M. Martelli (Eds.), Proc. 6th Int. Conf. and Symposium on Logic Programming, The MIT Press, 1989.Google Scholar
  40. 40.
    Myaeng S. H. and A. Lopez-Lopez, A Flexible Algorithm for Matching Conceptual Graphs, in the 6 Int Workshop on CGS, 1991 (see also a chapter by S. H. Myaeng in Nagle et al. (eds.), 1992).Google Scholar
  41. 41.
    Nagle T. E., J. W. Esch et G. Mineau, A Notation for Conceptual Structure Graph Matchers, in the 5 Int. Workshop on CGS, 1990 (see a chapter of the authors in Nagle et al. (eds.), 1992).Google Scholar
  42. 42.
    Nagle T. E., J. A. Nagle, L. L. Gerholz and P. W. Eklund, Conceptual Structures: Current research and practice, Ellis Horwood, 1992.Google Scholar
  43. 43.
    Pletat U. and K. von Luck, Knowledge Representation in LILOG, in Bläsius et al. (eds.), 1990.Google Scholar
  44. 44.
    Rao A. S. and N. Y. Foo, CONGRES: Conceptual Graph Reasoning System, Proc. IEEE, 1987.Google Scholar
  45. 45.
    J. F. Sowa, Conceptual Structures: Information Processing in Mind and Machine, Addison-Wesley.Google Scholar
  46. 46.
    Sowa J. F. (1992), Conceptual Graphs as a universal knowledge representation, in E. Y. Rodin (ed), Special Issue on Semantic networks in artificial intelligence, in an International Journal computers & mathematics with applications, 23:2–9, 1992.Google Scholar
  47. 47.
    Van Marcke K., KRS: An Object Oriented Representation Language, Revue d'IA, 1:4, pp. 43–68, 1987.Google Scholar
  48. 48.
    Voinov A. V., Netlog — A Concept Oriented Logic Programming Language, in A. Voronkov (ed.), Logic Programming and Automated Reasoning, Springer-Verlag, 1992.Google Scholar
  49. 49.
    Woods W. A. et J. G. Schmolze, The KL-ONE family, in E. Y. Rodin (ed), Special Issue on Semantic networks in artificial intelligence, in an International Journal computers & mathematics with applications, 23:2–9, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Adil Kabbaj
    • 1
  • Claude Frasson
    • 1
  • Marc Kaltenbach
    • 1
  • Jean-Yves Djamen
    • 1
  1. 1.Département d'informatique et de recherche opérationnelle Groupe HeronUniversité de MontréalSucc. A

Personalised recommendations