INFERENCE PROCEDURES FOR FUZZY KNOWLEDGE REPRESENTATION SCHEME

REFERENCES

• Aiello , L. C. and D. Nardi . 1991 . Perspectives in knowledge representation . Applied Artificial Intelligence 5 ( 1 ): 29 – 44 .
   Web of Science ®Google Scholar
• 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 .
   Google Scholar
• 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 .
   Google Scholar
• Canales , J. C. , 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 .
   Google Scholar
• Cardoso , J. and H. Camargo (eds.). 1999 . Fuzziness in Petri Nets . Heidelberg : Physica-Verlag .
   Google Scholar
• 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 .
   Google Scholar
• Chen , S.-M. 2002 . Weighted fuzzy reasoning using weighted fuzzy petri nets . IEEE Trans. on Knowledge and Data Engineering 14 ( 2 ): 386 – 397 .
   Google Scholar
• Dahl , V. 1983 . Logic programming as a representation of knowledge . Computer 16 ( 10 ): 106 – 111 .
   Google Scholar
• 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 .
   Google Scholar
• Findler , N. V. (ed.). 1979 . Associative Networks , New York : Academic Press .
   Google Scholar
• Garcia , O. N. and Y.-T. Chien (ed.). 1991 . Knowledge-Based Systems: Fundaments and Tools . Los Alamitos : IEEE Computer Society Press .
   Google Scholar
• 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 .
   Google Scholar
• 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 .
   Google Scholar
• Israel , D. J. and B. Beranek . 1983 . The role of logic in knowledge representation . Computer 16 ( 10 ): 37 – 41 .
   Google Scholar
• 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 .
   Google Scholar
• Li , X. and F. Lara-Rosano . 2000 . Adaptive fuzzy petri nets for dynamic knowledge representation and inference . Expert Systems with Applications 19 : 235 – 241 .
   Web of Science ®Google Scholar
• Looney , C. G. 1988 . Fuzzy petri nets for rule-based decision making . IEEE Trans. on the System, Man and Cybernetics 18 ( 1 ): 178 – 183 .
   Web of Science ®Google Scholar
• 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 .
   Google Scholar
• Minsky , M. L. 1975 . A framework for representing knowledge . In: The Psychology of Computer Vision ed. P. H. Winston , New York : McGraw-Hill .
   Google Scholar
• Newell , A. and H. A. Simon . 1972. Human Problem Solving . Englewood Cliffs , NJ : Prentice-Hall.
   Google Scholar
• Pearl , J. 1988 . Probabilistic Reasoning in Intelligent Systems: Network of Plausible Inference . San Mateo : Morgan Kaufmann .
   Google Scholar
• Peterson , J. L. 1977 . Petri nets , ACM Computing Surveys 3 ( 9 ): 223 – 253 .
   Google Scholar
• Peterson , J L. 1981 . Petri net theory and Modeling of Systems . Englewood Cliffs , NJ : Prentice-Hall .
   Google Scholar
• Petri , C. A. 1962 . Kommunikation mit Automaten . Bonn : Institut für 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 .
   Google Scholar
• Quillian , M. R. 1967 . Word concepts: A theory and simulation of some basic semantic capabilities . Behavioral Science 12 ( 5 ): 410 – 430 .
   PubMedGoogle Scholar
• Ribarić , S. 1991 . An original knowledge representation scheme . Automatika 1–2 ( 32 ): 13 – 24 .
   Google Scholar
• Russel , S. and P. Norvig . 1995 . Artificial Intelligence, A Modern Approach . Upper Saddle River , NJ : Prentice Hall .
   Google Scholar
• 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 .
   Google Scholar
• Shafer , G. 1976 . Mathematical Theory of Evidence . Princeton , NJ : Princeton Univ. Press .
   Google Scholar
• Shastri , L. 1988 . Semantic Networks: An Evidential Formalization and Its Connectionist Realization . London : Pitman .
   Google Scholar
• 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 .
   Google Scholar
• Touretzky , D. S. 1986 . The Mathematics of Inheritance Systems . London : Pitman .
   Google Scholar
• 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 .
   Google Scholar
• Zeigler , B. P. 1987 . Knowledge representation from Newton to Minsky and beyond . Applied Artificial Intelligence 1 : 87 – 107 .
   Google Scholar