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Structuring Knowledge In Vague Domains
February 1995 (vol. 7 no. 1)
pp. 120-136

Abstract—In this paper, we propose a model for structuring knowledge in vague and continuous domains where similarity plays a role in coming up with plausible inferences. The model consists of two levels, one of which is an inference network with nodes representing concepts and links representing rules connecting concepts, and the other is a microfeature-based replica of the first level. Based on the interaction between the concept nodes and microfeature nodes in the model, inferences are facilitated and knowledge not explicitly encoded in a system can be deduced via mixed similarity matching and rule application. The model is able to take account of many important desiderata of plausible reasoning and produces sensible conclusions accordingly. Examples will be presented to illustrate the utility of the model in structuring knowledge to enable useful inferences to be carried out in several domains.

[1] Neurocomputing, J. Anderson and E. Rosenfeld eds., Cambridge, Mass.: MIT Press, 1988.
[2] L. Bookman,“A connectionist scheme for modeling context,, D. Touretzky et al. eds., Proc. 1988 Connectionist Summer School,San Mateo, CA: Morgan Kaufman, 1989, pp. 281-290
[3] C.L. Chang, and R.C.T. Lee,Symbolic Logic and Mechanical Theorem Proving.New York: Academic Press, 1973.
[4] A. Collins and R. Michalski,“The logic of plausible reasoning: A core theory,” Cognitive Science, vol. 13, no. 1, pp. 1-49, 1989.
[5] M. Derthick,“Mundane reasoning by parallel constraint satisfaction,” TR CMU-CS-88-182, Carnegie Mellon University, 1988.
[6] D. Dubois and H. Prade,“An introduction to possibilistic and fuzzy logics,” P. Smets et al. eds., Non-Standard Logics for Automated Reasoning,San Diego, CA.: Academic Press, 1988.
[7] J. Feldman,Neural Representation of Conceptual Knowledge, Technical Report 189, Dept. of Computer Science, Univ. of Rochester, 1986.
[8] L. Fu,“Recognition of Semantically Incorrect Rules,” Proc. IEA/AIE-90, 1990.
[9] S. Gallant,“Connectionist expert systems,” Communication of ACM, v.31(2), pp. 152-169, 1988.
[10] J. Gelfand,D. Handelman,, and S. Lane,“Integrating knowledge-based systemsand neural networks for robotic skill acquisition,” Proc. IJCAI,San Mateo, CA.: Morgan Kaufman, 1989, pp. 193-198.
[11] S. Grossberg,The Adaptive Brain,New York, NY: North-Holland, 1987.
[12] P.J. Hayes,“In defence of logic,” Proc. Fifth IJCAI,San Mateo, CA.: Morgan Kaufman, 1977, pp. 559-565.
[13] Building Expert Systems, F. Hayes-Roth,D.A. Waterman,, and D.B. Lenat, eds., Reading, MA.: Addison-Wesley, 1983.
[14] J. Hendler,“, Marker passing and microfeature,” Proc. 10th IJCAI,San Mateo, CA.: Morgan Kaufmann, 1987, pp. 151-154.
[15] J. Hendler, Integrating Marker-Passing and Problem-Solving.Hillsdale, N.J.: Lawrence Erlbaum Associates, 1988.
[16] K. Hornik, M. Stinchcombe, and H. White, “Multilayer Feedforward Networks are Universal Approximations,” Neural Networks, vol. 2, pp. 359-366, 1989.
[17] R. Lacher et al., “Backpropagation learning in expert networks,” IEEE Transactions on Neural Networks, vol. 3, pp. 62-72, 1992.
[18] T. Lange and M. Dyer,“Frame selection in a connectionist model,” Proc. 11th Cognitive Science Conference, Lawrence Erlbaum Associates, 1989, pp. 706-713.
[19] H. Leonard,Principle of Reasoning,New York, NY: Dover, 1967.
[20] R. Michalski,“Two-tiered concept meaning, inferential matching, andconceptual cohesiveness,” S. Vosniadou&J. Ortony, eds., Similarity and Analogical Reasoning,New York, NY: Cambridge University Press, 1989.
[21] M. Minsky, The Society of Mind, Simon and Schuster, New York, 1985.
[22] J. Pearl, Probabilistic Reasoning in Intelligent Systems. San Mateo, Calif.: Morgan Kaufman, 1988.
[23] Foundations of Cognitive Science, M. Posner, ed., Cambridge, MA: MIT Press, 1989.
[24] J. Pustejovsky,Generative Lexicon,Cambridge, MA: MIT Press, 1992.
[25] C.K. Riesbeck and R.C. Shank, Inside Case-Based Reasoning.N.J.: Lawrence Erlbaum Associates, 1989.
[26] D.EE. Rumelhart and J.L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, Mass., 1986.
[27] G. Shafer,A Mathematical Theory of Evidence,Princeton, NJ: Princeton University Press, 1974.
[28] R. Sun,“A discrete neural network model for conceptual representation andreasoning,” Proc. 11th Cognitive Science Society Conference, pp. 916-923,Hillsdale, NJ: Erlbaum, 1989 a.
[29] R. Sun,“Designing inference engines based on a discrete neural network model,” Proc. IEA/AIE,New York, NY: ACM Press, p. 1094, 1989 b.
[30] R. Sun,“Rules and connectionism,” Proc. INNC-Paris, p. 545,Netherland: Kluwer, 1990 a.
[31] R. Sun,“The discrete neuronal models,” Proc. INNC-Paris, pp. 902-907,Netherland: Kluwer, 1990 b.
[32] R. Sun,“The discrete neuronal models and the discrete neuronal models,” B. Soucek, ed., Neural and Intelligent System Integration,New York, NY: John Wiley&Sons, 1991 a.
[33] R. Sun,“Chunking and connectionism,” Neural Network Review, vol. 4, no. 2, pp. 76-78, 1991 b.
[34] R. Sun and D. Waltz,“Neurally inspired massively parallel model of rule-based reasoning,” B. Soucek, ed., Neural and Intelligent System Integration,New York, NY: John Wiley&Sons, 1991.
[35] R. Sun,“A connectionist model of commonsense reasoning incorporating rules and similarities,” Knowledge Acquisition, vol. 4, pp. 293-321, 1992.
[36] R. Sun,“Beyond associative memories, logics and variables in connectionist networks,” Information Sciences, vol. 70, 1993.
[37] D. Touretzky,The Mathematics of Inheritance,San Mateo, CA: Morgan Kaufman, 1986.
[38] G. Towell,J. Shavlik,, and M. Noordewier,“Refinement of approximate domain theories by knowledge-based neural networks,” Proc. AAAI-90,San Mateo, CA: Morgan Kaufmann, 1990, pp. 861-866.
[39] A. Tversky,“Features of Similarity,” Psychological Review, 84(4), pp. 327-352, 1977.
[40] S. Vosniadou and A. Ortony, Similarity and Analogical Reasoning, Cambridge University Press, Cambridge, UK, 1989.
[41] D. Waltz and J. Pollack,“Massively parallel parsing,” Cognitive Science, 1985.
[42] L. Zadeh,“Fuzzy sets,” Information and Control, vol. 8, pp. 338-353, 1965.
[43] L. Zadeh,“Test-score semantics for natural languages and meaning-representation via PRUF,” B. Rieger, ed., Empirical Semantics,Bochum: Brockmeyer, 1981, pp. 281-349.
[44] L. Zadeh,“Fuzzy logic,” Computer, vol. 21, no. 4, pp. 83-93, April, 1988.
[45] M. Waterman,An Introduction to Expert Systems,Reading, MA: Addison-Wesley, 1985.

Index Terms:
Artificial intelligence, knowledge-based systems, neural networks, reasoning, knowledge representation, vagueness.
Citation:
Ron Sun, "Structuring Knowledge In Vague Domains," IEEE Transactions on Knowledge and Data Engineering, vol. 7, no. 1, pp. 120-136, Feb. 1995, doi:10.1109/69.368514
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