## Abstract

In the last chapter, we discussed a crude approach to analyzing the behavior of a system by means of a coarse, qualitative, structural description of the real physical system. The claim was that this models the way humans reason about processes, and since humans are very adept at making correct decisions on the basis of incomplete knowledge, this approach may enable algorithms to duplicate such aptitude. It turned out that the results were not as promising as some researchers would like us to believe. Strong indicators can also be found that humans mostly assess the behavior of a system not on the basis of qualitative physical considerations, but on the basis of analogies with similar processes, the operation of which they have previously observed, i.e., that they use pattern recognition to analyze system behavior. In this chapter, we shall discuss one of several pattern recognition techniques that may be able to mimic how humans apply pattern recognition to reason about system behavior.

## Keywords

Shannon Entropy Inductive Reasoning Fuzzy Measure State Transition Matrix Hankel Matrix## Preview

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## References

- [13.1]François E. Ceffier (1987), “Prisoner’s Dilemma Revisited — A New Strategy Based on the General System Problem Solving Framework,”
*International J. of General Systems*,**13**(4), pp. 323–332.CrossRefGoogle Scholar - [13.2]François E. Cellier (1987), “Qualitative Simulation of Technical Systems Using the General System Problem Solving Framework,”
*International J. of General Systems*,**13**(4), pp. 333–344.Google Scholar - [13.3]François E. Cellier (1991), “General System Problem Solving Paradigm for Qualitative Modeling,” in:
*Qualitative Simulation*,*Modeling*,*and Analysis*( P.A. Fishwick and P.A. Luker, eds.), Springer-Verlag, New York, pp. 51–71.Google Scholar - [13.4]François E. Cellier and David W. Yandell (1987), “SAPS-II: A New Implementation of the Systems Approach Problem Solver,”
*International J. of General Systems*,**13**(4), pp. 307–322.CrossRefGoogle Scholar - [13.5]George J. Klir (1985),
*Architecture of Systems Problem Solving*, Plenum Press, New York.MATHCrossRefGoogle Scholar - [13.6]George J. Klir (1989), “Inductive Systems Modelling: An Overview,”
*Modelling and Simulation Methodology: Knowledge Systems’ Paradigms*(M.S. Elzas, T.I. Oren, and B.P. Zeigler, eds.), Elsevier Science Publishers B. V. ( North-Holland), Amsterdam, The Netherlands.Google Scholar - [13.7]Averill M. Law and W. David Kelton (1990),
*Simulation Modeling and Analysis*, second edition, McGraw-Hill, New York.Google Scholar - [13.8]DongHui Li and François E. Cellier (1990) “Fuzzy Measures in Inductive Reasoning,”
*Proceedings.1990 Winter Simulation Conference*, New Orleans, La., pp. 527–538.Google Scholar - [13.9]Glenn Shafer (1976), A
*Mathematical Theory of Evidence*, Princeton University Press, Princeton, N.J.Google Scholar - [13.10]Claude E. Shannon and Warren Weaver (1964),
*The Mathematical Theory of Communication*, University of Illinois Press, Urbana.Google Scholar - [13.11]Hugo J. Uyttenhove (1979),
*SAPS — System Approach Problem Solver*, Ph.D. dissertation, SUNY Binghampton, N.Y.Google Scholar - [13.12]Pentti J. Vesanterii and François E. Cellier (1989), “Building Intelligence into an Autopilot — Using Qualitative Simulation to Support Global Decision Making,”
*Simulation*,**52**(3), pp. 111–121.CrossRefGoogle Scholar - [13.13]Lotfi A. Zadeh (1985), “Syllogistic Reasoning in Fuzzy Logic and Its Application to Usuality and Reasoning with Dispositions,”
*IEEE Trans. Systems*,*Man*,*and Cybernetics*,SMC-15(6), pp. 754–763.Google Scholar - [13.14]Lotfi A. Zadeh (1986), “A Simple View of the Dempster-Shafer Theory of Evidence and Its Implication for the Rule of Combination,”
*The AI Magazine*, Summer Issue, pp. 85–90.Google Scholar - [13.15]Lotfi A. Zadeh (1987), “A Computational Theory of Dispositions,”
*International J. of Intelligent Systems*,**2**, pp. 39–63.MATHGoogle Scholar

## Bibliography

- [B13.1]Russell L. Ackoff (1978),
*The Art of Problem Solving*, WileyInterscience, New York.Google Scholar - [B13.2]Ludwig von Bertalanffy (1969),
*General System Theory: Foundations*,*Development*,*Applications*, G. Braziller Publishing, New York.Google Scholar - [B13.3]G. Broekstra (1978), “On the Representation and Identification of Structure Systems,”
*International J. of Systems Science*,**9**(11), pp. 1271–1293.MATHCrossRefGoogle Scholar - [B13.4]Brian R. Gaines (1979), “General Systems Research: Quo Vadis,”
*General Systems Yearbook*,**24**, pp. 1–9.Google Scholar - [B13.5]George J. Klir, Ed. (1978),
*Applied General Systems Research*, Plenum Press, New York.MATHGoogle Scholar - [B13.6]Allan Newell and Herbert A. Simon (1972), Human
*Problem Solving*, Prentice—Hall, Englewood Cliffs, N.J.Google Scholar - [B13.7]John L. Pollock (1990),
*Monic Probability and the Foundations of Induction*, Oxford University Press, New York.Google Scholar - [B13.8]Joseph E. Robertshaw, S. J. Mecca, and M. N. Rerick (1979),
*Problem Solving: A Systems Approach*, McGraw—Hill, New York.Google Scholar - [B13.9]Herbert A. Simon (1972), “Complexity and the Representation of Patterned Sequences of Symbols,”
*Psychological Reviews*,**79**, pp. 369–382.CrossRefGoogle Scholar - [B13.10]Herbert A. Simon (1977), Models of Discovery and Other Topics in the Methods of Science, Reidel Publishing, Boston, Mass.Google Scholar
- [B13.11]G. Towner (1980),
*The Architecture of Knowledge*, University Press of America, Washington, D.C.Google Scholar