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Inverting resolution with conceptual graphs

  • Maurice Pagnucco
  • Norman Foo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 699)

Abstract

Methods for performing inductive inference have become very important in Artificial Intelligence, especially in the area of Machine Learning. One technique capable of performing induction is based on inverting the resolution process (with the help of an oracle). This is known as inverse resolution.

In this paper we investigate how inverse resolution can be performed using conceptual graphs. This is done by showing how the individual inverse resolution operators can be implemented using conceptual graphs. We show that the processes involved can actually be viewed as inverses of beta (and alpha) rules. Also, the operations can be seen as analogues to inverse resolution operators suggested, in the literature, for predicate calculus (e.g., absorption, identification, etc.).

The advantage of this approach is that it develops a technique for performing induction using conceptual graphs. In particular, two of the operators are capable of performing constructive induction through the introduction of new relations not present in the original graphs. We also claim that the use of conceptual graphs provides a natural way of performing these operations and that this leads to a better understanding of the processes involved.

Keywords

Inductive inference inverse resolution machine learning constructive induction 

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References

  1. 1.
    Stephen Muggleton. Duce, an oracle based approach to constructive induction. In Proceedings of the 10th International Joint Conference on Artificial Intelligence, pages 287–292. Morgan Kaufman, 1987.Google Scholar
  2. 2.
    Stephen Muggleton. Inverting the resolution process. In J. E. Hayes and D. Michie, editors, Machine intelligence 12. 1989.Google Scholar
  3. 3.
    Stephen Muggleton and Wray Buntine. Machine invention of first-order predicates by inverting resolution. In Proceedings of the 5th International Machine Learning Workshop, pages 339–352. Morgan Kaufman, 1988.Google Scholar
  4. 4.
    Celine Rouveirol and Jean Francois Puget. Beyond inversion of resolution. In Proceedings of the 7th International Machine Learning Workshop, pages 122–130. Morgan Kaufman, 1990.Google Scholar
  5. 5.
    John F. Sowa. Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading, MA, 1984.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Maurice Pagnucco
    • 1
  • Norman Foo
    • 1
  1. 1.Knowledge Systems Group, Basser Department of Computer ScienceUniversity of SydneyNSWAustralia

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