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A Connectionist Realization Applying Knowledge-Compilation and Auto-Segmentation in a Symbolic Assignment Problem

  • Holger G. Ziegeler
  • Karl W. Kratky
Conference paper
Part of the Informatik-Fachberichte book series (INFORMATIK, volume 252)

Abstract

A symbolic assignment problem has been solved by making use of the fact that it can be represented as a decomposable production system. We explain how the structure of the given constraint satisfaction problem (CSP) can be exploited to design a two component architecture of a neural net interacting with a scheduler. We describe the relation of problem parameters to the net design, using a feedforward net with error backpropagation. Different versions of the net design are contrasted. We discuss the advantages of our architecture and relate the results of the connectionist approach to a solving of the problem with backtrack search. The CSP was part of a case study, a knowledge-based system for the automatic configuration of telephone exchanges. An enlargement of the architecture and its application is foreseen.

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References

  1. [1]
    Abu-Mustafa, Y.S., Neural Networks for Computing?, AIP Conf Proc. 151, 1986, 1–6.CrossRefGoogle Scholar
  2. [2]
    Barnden, John A., Neural-Net Implementation of Complex Symbol-Processing in a Mental Model Approach to Syllogistic Reasoning, Proceedings of IJCAI-89, Detroit, Mich., 1989, 568–573.Google Scholar
  3. [3]
    Ekeberg, O., and A. Lansner, Automatic Generation of Internal Representations in a Probabilistic Artificial Neural Network, in: Neural Networks from Models to Applications ( Personnaz, L., and G. Dreyfus, eds.), Paris: IDSET, 1989, 179–186.Google Scholar
  4. [4]
    Haralick, R.M., and G.L. Elliott, Increasing Tree Search Efficiency for Constraint Satisfaction Problems, Artificial Intelligence 14 (3), 1980, 61–76.CrossRefGoogle Scholar
  5. [5]
    Kaindl, H., and H.G. Ziegeler, Some Aspects of Knowledge-Based Configuration, Proceedings AVIGNON ‘80: Expert systems & their applications, Specialized Conference: Artificial Intelligence, Telecommunications & Computer Systems, 1990, 41–54.Google Scholar
  6. [6]
    Lapedes, A. and R. Farber, Programming a Massively Parallel, Computation Universal System: Static Behaviour, AIP Conf. Proc. 151, 1986, 283–298.CrossRefGoogle Scholar
  7. [7]
    Mackworth, A.K., Constraint Satisfaction, in Encyclopedia of Artificial Intelligence ( Shapiro, S.C., ed.), New York, N.Y.: Wiley, 1987, 205–211.Google Scholar
  8. [8]
    Nilsson, N.J., Principles of Artificial Intelligence, Tioga Publ. Co., 1980.zbMATHGoogle Scholar
  9. [9]
    Quinlan, J.R., Learning efficient classification procedures and their application to chess end games: in: Machine Learning 2 (Michalski, R.S., J.G. Carbonell, and T.M. Mitchell, eds.), Palo Alto, Ca.: Tioga, 1984, 463–482.Google Scholar
  10. [10]
    Rumelhart, D., J. McClelland et al. (ed.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, Ma.: MIT Press, 1986.Google Scholar
  11. [11]
    Tagliarini, G.A. and E.W. Page, Solving Constraint Satisfaction Problems with Neural Networks, Proc. IEEE First International Conference on Neural Networks, San Diego, 1987.Google Scholar
  12. [12]
    Ziegeler, H.G., and H. Kaindl, A Cyclic Pattern Resulting from a Constraint Satisfaction Search, working paper, to be presented at the AAAI-90 Workshop on Constraint Directed Reasoning, July 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Holger G. Ziegeler
    • 1
  • Karl W. Kratky
    • 1
  1. 1.Institut für ExperimentalphysikUniversität WienWienAustria

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