Inference procedures for fuzzy knowledge representation scheme

Applied Artificial Intelligence

Volumn 23, Issue 1, 2009, Pages 16-43

  Ribaric, Slobodan ^a   Pavesic, Nikola ^b  

^a UNIVERSITY OF ZAGREB   (Croatia)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY INFERENCE; FUZZY SYSTEMS; PETRI NETS; SEMANTICS;

DYNAMICAL PROPERTIES; FORMAL MODELING; FUZZY KNOWLEDGE REPRESENTATION; FUZZY PETRI NETS; FUZZY RECOGNITION; INFERENCE ALGORITHM; PETRI NET THEORIES; REPRESENTATION SCHEMES; TIME COMPLEXITY;

KNOWLEDGE REPRESENTATION;

EID: 60649114680     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839510802379634     Document Type: Article

References (34)

1. Aiello, L. C. and D. Nardi. 1991. Perspectives in knowledge representation. Applied Artificial Intelligence 5(1):29-44.
2. Bobrow, D. G. 1985. If prolog is the answer, what is the question? Or what it takes to support al programming paradigms. IEEE Software Eng. 11 (11): 1401-1408.
3. Buchanan, B. G. and E. H. Shortliffe. 1984. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley.
4. Canales, J. G., X. Liand, and W. Yu. 2006. Fuzzy knowledge learning via adaptive fuzzy petri net with triangular function model. Intelligent Control and Automation. The Sixth World Congress on WCICA 1:4249-4253.
5. Cardoso, J. and H. Camargo (eds.). 1999. Fuzziness in Petri Nets. Heidelberg: Physica-Verlag.
6. Chen, S.-M., J.-S. Ke, and J.-F, Chang. 1990. Knowledge representation using fuzzy petri nets. IEEE Trans. on Knowledge and Data Engineering 2(3):311-319.
7. Chen, S.-M. 2002. Weighted fuzzy reasoning using weighted fuzzy petri nets. IEEE Trans. on Knowledge and Data Engineering 14(2):386-397.
8. Dahl, V. 1983. Logic programming as a representation of knowledge. Computer 16(10):106-111.
9. Duda, R., J. G. Gaschnig, and P. E. Hart. 1979. Model design in the PROSPECTOR consultant system for mineral exploration. In: Expert Systems in the Microelectronic Age, ed. D. Michie, 153-167, Edinburgh: Edinburgh Univ. Press.
10. Findler, N. V. (ed.). 1979. Associative Networks, New York: Academic Press.
11. Garcia, O. N. and Y-T. Chien (ed.). 1991. Knowledge-Based Systems: Fundaments and Tools. Los Alamitos: IEEE Computer Society Press.
12. Guo-Yan, H. 2006. Analysis of artificial intelligence based petri net approach to intelligent integration of design. Proc. of the International Conference on Machine Learning and Cybernetics: 1691-1695.
13. Ha, M.-H., Y. Li, H.-J. Li, and P. Wang. 2005. A new form of knowledge representation and reasoning. Proc. of the Fourth Int. Conf. on Machine Learning and Cybernetics, vol. 4 pp. 2577-2582, Guangzhou.
14. Israel, D. J. and B. Beranek. 1983. The role of logic in knowledge representation. Computer 16(10):37-41.
15. Lee, J., K. F. R. Liu, and W. Chaing. 2003. Model uncertainty reasoning with possibilistic petri nets. IEEE Trans.on Systems, Man and, Cybernetics - Part B: Cybernetics 33(2):214-224.
16. Li, X. and F. Lara-Rosano. 2000. Adaptive fuzzy petri nets for dynamic knowledge representation and inference. Expert Systems with Applications 19:235-241.
17. Looney, C. G. 1988. Fuzzy petri nets for rule-based decision making. IEEE Trans. on the System, Man and Cybernetics 18(1):178-183.
18. Looney, C. G. and L. R. Liang. 2003. Inference via fuzzy belief petri nets. Proceed.of the 15th Int. Conf on Tools with Artificial Intelligence (ICTAI'03) 510-514.
19. Minsky, M. L. 197.5. A framework for representing knowledge. In: The Psychology of Computer Vision ed. P. H. Winston, New York: McGraw-Hill.
20. Newell, A. and H, A. Simon. 1972. Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall,
21. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference. San Mateo: Morgan Kaufmann.
22. Peterson, J. L. 1977. Petri nets, ACM Computing Surveys 3(9):223-253.
23. Peterson, J L. 1981. Petri net theory and Modeling of Systems. Englewood Cliffs, NJ: Prentice-Hall.
24. Petri, C. A, 1962. Kommunikation mit Automate. Bonn: Institut fur Instrumentelle Mathematik, Schriften des IIM Nr. 2. Second Edition: New York: Griffiss Air Force Base, Technical Report RADC-TR-65-377, 1, 1966: Suppl. 1.
25. Quillian, M. R. 1967. Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science 12(5):410-430.
26. Ribarić, S. 1991. An original knowledge representation scheme. Automatika 1-2(32):13-24.
27. Russel, S. and P. Norvig. 1995. Artificial Intelligence, A Modern Approach. Upper Saddle River, NJ: Prentice Hall.
28. Scarpelli, H., F. Gomide, and R. R. Yager. 1996. A reasoning algorithm for high level fuzzy petri nets. IEEE Trans. on Fuzzy Systems 4(3):282-294.
29. Shafer, G. 1976. Mathematical Theory of Evidence. Princeton, NJ: Princeton Univ. Press.
30. Shastri, L. 1988. Semantic Networks: An Evidential Formalization and Its Connectionist Realization. London: Pitman.
31. Shen, V. R. L. 2006. Knowledge representation using high-level fuzzy petri nets. IEEE Trans. on Systems, Man, and Cybernetics-Part A 36(6):1220-1227.
32. Touretzky, D. S. 1986. The Mathematics of Inheritance Systems. London: Pitman.
33. Zadeh, L. A. 1983. Role of fuzzy logic in the management of uncertainty in expert systems. In: Fuzzy Sets and Systems. Pp. 199-227. New York: Elsevier-North Holland.
34. Zeigler, B. P. 1987. Knowledge representation from Newton to Minsky and beyond. Applied Artificial Intelligence 1: 87-107.