Connectionist inference systems

  • Hans Werner Güsgen
  • Steffen Hölldobler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 590)


This paper presents a survey of connectionist inference systems.


Entropy Ghost Cond Ster Metaphor 


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  1. [Adorf and Johnston, 1990]
    H. M. Adorf and M. D. Johnston. A discrete stochastic neural network algorithm for constraint satisfaction problems. In Proceedings of the International Joint Conference on Neural Networks, 1990.Google Scholar
  2. [Ajjanagadde, 1990]
    V. Ajjanagadde. Reasoning with function symbols in a connecionist system. In Proceedings of the Annual Conference of the Cognitive Science Society, 1990.Google Scholar
  3. [Anandan et al, 1989]
    [Anandan et al., 1989] P. Anandan, S. Letovsky, and E. Mjolsness. Connectionist variable-binding by optimization. In Proceedings of the Annual Conference of the Cognitive Science Society, pages 388–395, 1989.Google Scholar
  4. [Ballard, 1986a]
    D. H. Ballard. Parallel logic inference and energy minimization. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 203–208, 1986.Google Scholar
  5. [Ballard, 1986b]
    D. H. Ballard. Parallel logic inference and energy minimization. Technical Report TR 142, Computer Science Department, Univ. of Rochester, Rochester, NY 14627, 1986.Google Scholar
  6. [Barnden, 1984]
    J. A. Barnden. On short term information processing in connectionist theories. Cognition and Brain Theory, 7:25–59, 1984.Google Scholar
  7. [Barnden, 1989]
    J. A. Barnden. Neural-net implementation of complex symbol-processing in a mental model approach to syllogistic reasoning. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 568–573, 1989.Google Scholar
  8. [Barto, 1983]
    [Barto et al., 1983] A. G. Barto, R. S. Sutton, and C. W. Anderson. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, pages 835–846, 1983.Google Scholar
  9. [Bibel, 1987]
    W. Bibel. Automated Theorem Proving. Vieweg Verlag, Braunschweig, second edition, 1987.Google Scholar
  10. [Bibel, 1990]
    W. Bibel. Perspectives in automated deduction. In R. S. Boyer, editor, Festschrift for W. W. Bledsoe. Kluwer Academic, Utrecht, 1990.Google Scholar
  11. [Bibel, to appear 1991]
    W. Bibel. Intellectics. In S. C. Shapiro, editor, Encyclopedia of Artificial Intelligence. John Wiley, New York, to appear 1991.Google Scholar
  12. [Brachman and Schmolze, 1985]
    R. J. Brachman and J. G. Schmolze. An overview of the KL-ONE knowledge representation system. Cognitive Science, 9(2): 171–216, 1985.Google Scholar
  13. [Brandt et al., 1988]
    [Brandt et al., 1988] R. D. Brandt, Y. Wang, A. J. Laub, and S. K. Mitra. Alternative networks for solving the travelling salesman problem. In IEEE International Conference on Neural Networks, pages vol. II, 333–340, 1988.Google Scholar
  14. [Brewka, 1989]
    G. Brewka. Preferred subtheories: An extended logical framework for default reasoning. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1043–1048, 1989.Google Scholar
  15. [Chang and Lee, 1973]
    L. Chang and R. C. T. Lee. Symbolic Logic and Mechanical Theorem Proving. Academic Press, New York, 1973.Google Scholar
  16. [Churchland, 1986]
    P. S. Churchland. Neurophilosophy: Toward a Unified Science of Mind/Brain. MIT Press, Cambridge, Ma., 1986.Google Scholar
  17. [Cooper and Swain, 1988]
    P. R. Cooper and M. J. Swain. Parallelism and domain dependence in constraint satisfaction. Technical Report 255, Computer Science Department, Univ. of Rochester, 1988.Google Scholar
  18. [Cooper, 1989]
    P. R. Cooper. Parallel object recognition from structure (the tinkertoy project). Technical Report TR 301, University of Rochester, Computer Science Department, 1989.Google Scholar
  19. [Davis, 1984]
    R. Davis. Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24:347–410, 1984.Google Scholar
  20. [DeKleer and Williams, 1986]
    J. DeKleer and B. C. Williams. Diagnosing multiple faults. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 132–139, 1986.Google Scholar
  21. [Derthick, 1990]
    M. Derthick. Mundane reasoning by settling on a plausible model. Artificial Intelligence, 46:77–106, 1990.Google Scholar
  22. [Diederich, 1988]
    J. Diederich. Connectionist recruitment learning. In Proceedings of the European Conference on Artificial Intelligence, pages 351–356, 1988.Google Scholar
  23. [Dolan and Smolensky, 1988]
    C. P. Dolan and P. Smolensky. Implementing a connectionist production system using tensor products. In Touretzky, Hinton, and Sejnowski, editors, Proceedings of the 1988 Connectionist Models Summer School, pages 265–272. Morgan Kaufmann, 1988.Google Scholar
  24. [Elman, 1989]
    J. L. Elman. Structured representations and connectionist models. In Proceedings of the Annual Conference of the Cognitive Science Society, pages 17–25, 1989.Google Scholar
  25. [Feldman and Ballard, 1982]
    J. A. Feldman and D. H. Ballard. Connectionist models and their properties. Cognitive Science, 6(3):205–254, 1982.Google Scholar
  26. [Feldman, 1982]
    J. A. Feldman. Dynamic connections in neural networks. Biological Cybernetics, 46:27–39, 1982.Google Scholar
  27. [Geffner and Pearl, 1987]
    H. Geffner and J. Pearl. An improved constraint-propagation algorithm for diagnosis. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 1105–1111, 1987.Google Scholar
  28. [Geman and Geman, 1984]
    S. Geman and D. Geman. Stochastic relaxation, gibbs distribution, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.Google Scholar
  29. [Güsgen, 1990]
    H. W. Güsgen. Connectionist networks for constraint satisfaction. In Proceedings of the ISMM International Conference on Parallel and Distributed Computing, and Systems, pages 12–16, 1990.Google Scholar
  30. [Hadley, 1990]
    R. F. Hadley. Connectionism, rule following, and symbolic manipulation. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 579–586, 1990.Google Scholar
  31. [Hertz, 1991]
    [Hertz et al., 1991] J. Hertz, A. Krogh, and R. G. Palmer. Introduction to the Theory of Neural Computation. Addison-Wesley Publishing Company, 1991.Google Scholar
  32. [Hertzberg and Guesgen, 1991]
    J. Hertzberg and H.W. Guesgen. Transforming constraint relaxation networks into Boltzmann machines. In Proceedings of the German Workshop on Artificial Intelligence, pages 244–253. Springer, 1991.Google Scholar
  33. [Hinton and Sejnowski, 1983]
    G. E. Hinton and T. J. Sejnowski. Optimal perceptual inference. In Proceedings of the IEEE Conference on Computer Vision and Recognition, pages 448–453, 1983.Google Scholar
  34. [Hölldobler and Kurfess, 1991]
    S. Hölldobler and F. Kurfess. CHCL — A connectionist inference system. In B. Fronhöfer and G. Wrightson, editors, Parallelization in Inference Systems. Springer, 1991. (to appear).Google Scholar
  35. [Hölldobler, 1990a]
    S. Hölldobler. CHCL — A connectionist inference system for a limited class of Horn clauses based on the connection method. Technical Report TR-90-042, International Computer Science Institute, Berkeley, CA, 1990.Google Scholar
  36. [Hölldobler, 1990b]
    S. Hölldobler. A structured connectionist unification algorithm. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 587–593, 1990, A long version appeared as Technical Report TR-90-012, International Computer Science Institute, Berkeley, California.Google Scholar
  37. [Hölldobler, 1990c]
    S. Hölldobler. Towards a connectionist inference system. In Proceedings of the International Symposium on Computational Intelligence, 1990.Google Scholar
  38. [Hopfield and Tank, 1985]
    J. J. Hopfield and D. W. Tank. Neural computation of decisions in optimization problems. Biological Cybernetics, 52:141–152, 1985.Google Scholar
  39. [Hopfield, 1982]
    J. J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. In Proceedings of the National Academy of Sciences USA, pages 2554–2558, 1982.Google Scholar
  40. [Jaffar and Lassez, 1987]
    J. Jaffar and J-L. Lassez. Constraint logic programming. In Proceedings of the ACM Symposium on Principles of Programming Languages, pages 111–119, 1987.Google Scholar
  41. [Johnson-Laird and Bara, 1984]
    P.N. Johnson-Laird and B.G. Bara. Syllogistic inference. Cognition, 16(1):1–61, 1984.Google Scholar
  42. [Jordan, 1986]
    M. I. Jordan. Attractor dynamics and parallelism in a connectionist sequential machine. In Proceedings of the Annual Conference of the Cognitive Science Society, 1986.Google Scholar
  43. [Kamgar-Parsi and Kamgar-Parsi, 1990]
    B. Kamgar-Parsi and B. Kamgar-Parsi. On problem solving with hopfield neural nets. Biological Cybernetics, 62:415–423, 1990.Google Scholar
  44. [Kasif, 1990]
    S. Kasif. On the parallel complexity of discrete relaxation in constraint satisfaction networks. Artificial Intelligence, 45(3):275–286, 1990.Google Scholar
  45. [Kirkpatrick et al., 1983]
    [Kirkpatrick et al., 1983] S. Kirkpatrick, C. D. Gelatt Jr., and M. P. Vecchi. Optimization by simulated annealing. Science, 220:671–680, 1983.Google Scholar
  46. [Kleer, 1989]
    J. De Kleer. A comparison of ATMS and CSP techniques. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 290–296, 1989.Google Scholar
  47. [Kowalski, 1979]
    R. A. Kowalski. Algorithm = logic + control. Communications of the ACM, 22:424–436, 1979.Google Scholar
  48. [Lange and Dyer, 1989a]
    T. E. Lange and M. G. Dyer. Frame selection in a connectionist model of high-level inferencing. In Proceedings of the Annual Conference of the Cognitive Science Society, pages 706–713, 1989.Google Scholar
  49. [Lange and Dyer, 1989b]
    T. E. Lange and M. G. Dyer. High-level inferencing in a connectionist network. Connection Science, 1:181–217, 1989.Google Scholar
  50. [Lenat et al., 1990]
    [Lenat et al., 1990] D. B. Lenat, R. V. Guha, K. Pittman, D. Pratt, and M. Shepard. Cyc: Toward programming with common sense. Communications of the ACM, 33(8):30–49, 1990.Google Scholar
  51. [Letz et al., 1992]
    [Letz et al., 1992] R. Letz, S. Bayerl, J. Schumann, and W. Bibel. Setheo: A highperformance theorem prover. Journal of Automated Reasonsing, 1992. to appear.Google Scholar
  52. [Mackworth, 1977]
    A. K. Mackworth. Consistency in networks of relations. Artificial Intelligence, 8:99–118, 1977.Google Scholar
  53. [Mackworth, 1987]
    A. Mackworth. Constraint satisfaction. In Shapiro, editor, Encyclopedia of Artificial Intelligence, pages 205–211. John Wiley & Sons, 1987.Google Scholar
  54. [McClelland and Rumelhart, 1981]
    J. L. McClelland and D. E. Rumelhart. An interactive activation model of the effect of context in perception: Part 1. Psychological Review, 88:375–405, 1981.Google Scholar
  55. [McClelland et al.]
    [McClelland et al.,] J. L. McClelland, J. Feldman, G. Bower, and D. McDermott. Connectionist models and cognitive science: Goals, directions and implications.Google Scholar
  56. [McCulloch and Pitts, 1943]
    W. S. McCulloch and W. Pitts. A logical calculus and the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115–133, 1943.Google Scholar
  57. [Miller, 1956]
    G. A. Miller. The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81–97, 1956.Google Scholar
  58. [Minsky and Papert, 1972]
    M. Minsky and S. Papert. Perceptrons. MIT Press, 1972.Google Scholar
  59. [Minston et al., 1990]
    [Minton et al., 1990] S. Minton, M. D. Johnston, A. B. Philips, and P. Laird. Solving large-scale constraint-satisfaction and scheduling problems using a heuristic repair method. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 17–24, 1990.Google Scholar
  60. [Mjolsness et al., 1989]
    [Mjolsness et al., 1989] E. Mjolsness, G. Gindi, and P. Anandan. Optimization in model matching and perceptual organization. Neural Computation, 1:218–229, 1989.Google Scholar
  61. [Mozer, 1987]
    M. C. Mozer. The Perception of Multiple Objects: A Parallel, Distributed Proceesing Approach. PhD thesis, University of California, San Diego, 1987.Google Scholar
  62. [Pinkas, 1990]
    G. Pinkas. The equivalence of energy minimization and propositional calculus satisfiability. Technical Report WUCS-90-03, Washington University, 1990.Google Scholar
  63. [Pinkas, 1991a]
    G. Pinkas. Expressing first-order logic in symmetric connectionist networks. In L. N. Kanal and C. B. Suttner, editors, Informal Proceedings of the International Workshop on Parallel Processing for AI, pages 155–160, Sydney, Australia, August 1991 1991.Google Scholar
  64. [Pinkas, 1991b]
    G. Pinkas. Propositional non-monotonic reasoning and inconsistency in symmetrical neural networks. In Proceedings of the International Joint Conference on Artificial Intelligence, 1991.Google Scholar
  65. [Posner, 1978]
    M. I. Posner. Chronometrie Explorations of the Mind. Lawrence Erlbaum Associates, 1978.Google Scholar
  66. [Quillian, 1968]
    R. M. Quillian. Semantic memory. In Minsky, editor, Semantic Information Processing, pages 216–270. MIT Press, 1968.Google Scholar
  67. [Rumelhart and Zipser, 1985]
    D. E. Rumelhart and D. Zipser. Feature discovery by competitive learning. Cognitive Science, 9:75–112, 1985.Google Scholar
  68. [Rumelhart, 1986a]
    [Rumelhart et al., 1986a] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In Parallel Distributed Processing. MIT Press, 1986.Google Scholar
  69. [Rumelhart et al., 1986b]
    [Rumelhart et al., 1986b] D. E. Rumelhart, J. L. McClelland, and the PDP Research Group. Parallel Distributed Processing. The MIT Press, 1986.Google Scholar
  70. [Shastri and Ajjanagadde, 1990a]
    L. Shastri and V. Ajjanagadde. From associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings. Technical Report MS-CIS-90-05, Department of Computer and Information Science, University of Pennsylvania, Philadelphia, School of Engineering and Applied Science, PA 19104-6389, 1990.Google Scholar
  71. [Shastri and Ajjanagadde, 1990b]
    L. Shastri and V. Ajjanagadde. An optimally efficient limited inference system. In Proceedings of the AAAI National Conference on Artificial Intelligence, pages 563–570, 1990.Google Scholar
  72. [Shastri, 1988a]
    L. Shastri. A connectionist approach to knowledge representation and limited inference. Cognitive Science, 12(3), 1988.Google Scholar
  73. [Shastri, 1988b]
    L. Shastri. Semantic Networks: An Evidential Formalization and its Connectionist Realization. Research notes in Artificial Intelligence. Pitman, London, 1988.Google Scholar
  74. [Shastri, 1989a]
    L. Shastri. Connectionism, knowledge representation, and effective reasoning. In Brauer und Freksa, editor, Proceedings of the International GI Congress on Knowledge-Based Systems, pages 186–195, 1989.Google Scholar
  75. [Shastri, 1989b]
    L. Shastri. Default reasoning in semantic networks: A formalization of recognition and inheritance. Artificial Intelligence, 39:283–355, 1989.Google Scholar
  76. [Smolensky, 1988]
    P. Smolensky. On the proper treatment of connectionism. Behavioral and Brain Sciences, 11:1–74, 1988.Google Scholar
  77. [Smolensky, 1990]
    P. Smolensky. Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46:159–216, 1990.Google Scholar
  78. [Stallman and Sussman, 1977]
    R. M. Stallman and G. J. Sussman. Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence, 9:135–196, 1977.Google Scholar
  79. [Stefik, 1981]
    M. Stefik. Planning with constraints (molgen: part 1). Artificial Intelligence, 16:111–140, 1981.Google Scholar
  80. [Stickel, 1987]
    M. E. Stickel. An introduction to automated deduction. In W. Bibel and P. Jorrand, editors, Fundamentals of Artificial Intelligence, pages 75–132. Springer, 1987.Google Scholar
  81. [Suttner and Ertel, 1990]
    C. B. Suttner and W. Ertel. Using connectionist networks for guiding the search of a theorem prover. Journal of Neural Networks Research and Application, 1990.Google Scholar
  82. [Tomabechi and Kitano, 1989]
    H. Tomabechi and H. Kitano. Beyond PDP: The frequency modulation neural network architecture. In Proceedings of the International Joint Conference on Artificial Intelligence, pages 186–192, 1989.Google Scholar
  83. [Touretzky and Hinton, 1988]
    D. S. Touretzky and G. E. Hinton. A distributed connectionist production system. Cognitive Science, 12:423–466, 1988.Google Scholar
  84. [Touretzky, 1990]
    D. S. Touretzky. BoltzCONS: Dynamic symbol structures in a connectionist network. Artificial Intelligence, 46:5–46, 1990.Google Scholar
  85. [Ultsch et al., 1990]
    [Ultsch et al., 1990] A. Ultsch, R. Hannuschka, U. Hartmann, and V. Weber. Learning of control knowledge for symbolic proofs with backpropagation networks. In R. Eckmiller, G. Hartmann, and G. Hauske, editors, Parallel Processing in Neural Systems and Computers, pages 499–502. Elsevier, 1990.Google Scholar
  86. [Waltz, 1972]
    D. L. Waltz. Generating semantic descriptions from drawings of scenes with shadows. Technical Report AI-TR-271, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1972.Google Scholar
  87. [Wang and Tsang, 1991]
    C. J. Wang and E. P. K. Tsang. Solving constraint satisfaction problems using neural networks. Department of Computer Science, University of Essex, 1991.Google Scholar
  88. [Wilson and Pawley, 1988]
    G. V. Wilson and G. S. Pawley. On the stability of the travelling salesman problem algorithm of Hopfield and Tank. Biological Cybernetics, 58:63–70, 1988.Google Scholar

Copyright information

© Springer-Verlag 1992

Authors and Affiliations

  • Hans Werner Güsgen
    • 1
  • Steffen Hölldobler
    • 2
  1. 1.Gesellschaft für Mathematik und Datenverarbeitung (GMD)Sankt Augustin 1
  2. 2.TH DarmstadtFG Intellektik, FB InformatikDarmstadt

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