Researchers have felt that expert perfor mance must also rest on knowledge of deep models which relate underlying causal variables to observable facts. Simulation based upon causal knowledge is an impor tant method to infer possible consequences from given situations. This paper presents a rule-based causal simulation system called CAUSIM, which basically offers two kinds of simulation: backward simulation and forward simulation. Backward simulation is used to infer the instant behavior of specific attributes, whereas forward simulation is taken to arrive at possible overall scenarios. In addition, CAUSIM invokes constraint rules which describe incompatible behavior and values among related variables before applying simulation rules in order to obviate the inconsistencies between the simulation result and existing facts. The strength of CAUSIM lies in the capability of perform ing both qualitative and quantitative causal simulation in an integrated environment.
Blalock, H.M. , Jr., "Four variable causal models and partial correlation," American ]ournal of Sociology, 68,1962,182-194.
3.
Blalock, H.M., Jr., Causal Inferences in NonExperimental Research, The Norton Library, New York, 1964 .
4.
Blum, R.L., "Discovery and Representation of Causal Relationships from a Large Time-Oriented Clinical Database: The RX Project." Ph.D. thesis, Stanford U., 1982.
5.
Bobrow, D.G. (Ed.), Qualitative Reasoning about Physical Systems, The MIT press, Cambridge, MA, 1985.
6.
Cooper, G., "NESTOR: A computer-based medical diagnostic aid that integrates causal and probabilistic knowledge," Ph.D. thesis, Dept. Computer Science, Stanford U., 1984.
7.
Fishwick, P.A. , "The role of process abstraction in simulation ," IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 1988, 18-38.
8.
Forbus, KD, "Qualitative process theory," Artificial Intelligence24,1984.
9.
Glymour, C., Scheines, R., Spirtes, P., and Kelly, K., Discovering Causal Structure, Academic Press, 1987.
10.
Heise, D.R., Causal Analysis, John Wiley & Sons Inc., New York, 1975.
11.
Iwasaki, Y. and Simon, H.A., "Causality in device behavior ," Artificial Intelligence, 29, 1986 , 3-32.
12.
Kuipers, B. , "Qualitative simulation," Artificial Intelligence, 29, 1986 ,289-338.
13.
Kulikowski, C. and Weiss, S., "Representation of expert knowledge for consultation: The CASNET and EXPERT projects," in Artificial Intelligence in Medicine, Westview Press, Boulder, Colorado, 1982.
14.
Lee, N.S., "Programming with P-Shell, "IEEE Expert, Summer, 1986.
15.
Patil, R.S., Szolovits, P., and Schwartz, W.B., "Causal understanding of patient illness in medical diagnosis," 7th IJCAI, 1981, 893-899.
16.
Pearl, J., "Fusion, propagation, and structuring in belief networks," Artificial Intelligence29, 1986 , 241-288.
17.
Pearl, J., "Evidential reasoning using stochastic simulation of causal models ," Artificial Intelligence, 32(2), 1987, 245-257.
18.
Peng, Y. and Reggia, J.A., "A probabilistic causal model for diagnostic problem solving, Part I: Integrating symbolic causal inference with numeric probabilistic inference ," IEEE Transactions on Systems, Man, and Cybernetics, Vol. SMC-17, No. 2, 1987,146-162.
19.
Simon, H.A. , Spurious correlations: A causal interpretation , J. Am. Stat. Assoc.49, 1954,469-492.
20.
Szolovits, P. (ed.), Artificial Intelligence in Medicine, Westview Press, Boulder, Colorado, 1982.
21.
Weiser-Friedman, L. and Pressman, I., "The metamodel in simulation analysis: Can it be trusted?" in Operations Research, 1989 ,231-243.
22.
Zeigler, B.P. , Theory of Modeling and Simulation, John Wiley, New York, 1976.
23.
Zeigler, B.P. , Multi-Faceted Modeling and Discrete Event Simulation , Academic Press, New York, 1984.
