[1]Bonisonne P. (1985) Reasoning with uncertainty in expert systems. International Journal of Man-Machine Studies, 22: 3.

2Cohen P. (1985) Heuristic reasoning about uncertainty: an artificial intelligence approach. London: Pitman.

3Cohen P. and Gruber T. (1985) Reasoning about uncertainty: A knowledge representation perspective. In John Fox (Ed.) Pergamon Infotech State of the Art Report.

4Cohen P., Shafer G. and Shenoy P. (1987) Modifiable combining functions. COINS Technical Report 87–43, Department of Computer and Information Science, University of Massachusetts, Amherst, MA.

5Duda R.O., Hart P.E. and Nilsson N. (1976) Subjective Bayesian methods for rule-based inference systems. Technical note 124, AI Center, SRI International, Menlo Park, CA.

6Gordon J. and Shortliffe E.H. (1984) The Dempster-Shafer theory of evidence and its relevance to expert systems. The MYCIN Experiments of the Stanford Heuristic Programming Project, Reading, MA: Addison-Wesley.

[7]Henrion Max (1986) Should we use probability in uncertain inference systems. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, pp. 320–331.

8Lowrance John D. and Garvey Thomas D. (1983) Evidential reasoning: an implementation for multi-sensor integration. Technical Note 307, AI Center, SRI International, Menlo Park, CA.

[9]Pearl J., Leal A. and Saleh J. (1982) GODDESS: A Goal-Directed Decision Structuring System. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 4, pp. 250–262.

[10]Quinlan J.R. (1983) INFERNO: A cautious approach to uncertain inference. Computer Journal, vol. 26.

11Shafer G. (1976) A mathematical theory of evidence. Princeton: Princeton University Press.

12Shafer G. (1984) Probability judgment in artificial intelligence and expert systems. University of Kansas Working Paper 165, Lawrence School of Business, Lawrence, Kansas.

13Shafer G. and Tversky A., (1986) Languages and designs for probability judgment. Cognitive Science, in press.

14Shortliffe E.H. and Buchanan B.G. (1984) A model of inexact reasoning in medicine, Rule Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Reading, MA: Addison-Wesley.

[15]Sullivan M. and Cohen P.R. (1985) An endorsement-based plan recognition program. Proceedings of the international Joint Conference on Artificial Intelligence, pp. 475–479.

[16]Szolovits P. and Pauker S.G. (1978) Categorical and Probabilistic Reasoning in Medical Diagnosis. Artificial Intelligence, vol. 11, pp. 115–144.

[1]Chandrasakeran B. (1986) Generic tasks in knowledge-based reasoning: high-level building blocks for expert system design. IEEE Expert, Fall, pp. 23–30.

[2]Bylander T. and Mittal S.CSRL: A language for classificatory problem solving and uncertainty handling. AI Magazine. 7(3): 66–77, 1986.

[3]Clancey W.J.Heuristic Classification. Artificial Intelligence. 27: 289–350, 1985.

[4]Clancey W.J.From GUIDON to NEOMYCIN and HERACLES in twenty short lessons. AI Magazine. 7(3): 40–60, 1986.

5Cohen P.R., Greenberg M., DeLisio J. (1987) MU: A development environment for prospective reasoning systems. AAAI-87, 07, 1987, Seattle, WA.

[6]Gruber T. and Cohen P.R. (1987a) Design for acquisition: principles of knowledge system design to facilitate knowledge acquisition. The International Journal of Man-Machine Studies, forthcoming, Spring, 1987.

7Gruber T. and Cohen P.R. (1987b) Knowledge engineering tools at the architecture level. (revised version) 10th International Joint Conference on Artificial Intelligence. Milan, Italy. 08.

8Hayes-Roth B., Garvey A., Johnson M.V. and Hewett M. A layered environment for reasoning about action. Report No. KSL 86–38. Computer Science Department, Stanford University.

9Kahn G., Nowlan S. and McDermott J. MORE: An intelligent knowledge acquisition tool. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA, 08, 581–584.

10Marcus S., McDermott J. and Wang T. Knowledge acquisition for constructive systems. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA, 08, 1985. pp. 637–640.

1Cohen P.R. and Feigenbaum E.A. (1982) The Handbook of Artificial Intelligence, 3, Reading, MA: Addison-Wesley.

[2]Fikes R., Hart P. and Nilsson N.Learning and executing generalized robot plans. Artificial Intelligence, 3(4): 251–288. 1972.

3Fox John (1985) Knowledge decision making and uncertainty. Workshop on Artificial Intelligence and Statistics, Bell Laboratories.

4Howard R.A. Decision analysis: applied decision theory, Hertz D.B. and Melese J., editors, Proceedings of the Fourth International Conference on Operational Research. 55–71, New York: Wiley. 1966.

5Howe A. and Cohen P.R.1986. A typology for constructing decisions. Proceedings of IEEE Conference on Computers and Communications. Phoenix, AZ, 02, 1987.

6Raiffa H.Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Reading, MA: Addison-Wesley. 1970.

[7]Sacerdoti E. (1979) Problem solving tactics. Proceedings of the Sixth international Joint Conference on Artificial Intelligence, pp. 1077–1085.

8Waltz D. Generating semantic descriptions from drawings of scenes with shadows. Reprinted in Winston P. (Ed.) The psychology of computer vision. New York: McGraw-Hill. 19–92, 1975.

1Clancey W.J. and Letsinger R. NEOMYCIN: Reconfiguring a rule-based expert system for application to teaching. In Clancey W.J. and Shortcliffe E.H. (Eds.) Readings in Medical Artificial Intelligence: The First Decade. Reading, MA.: Addison-Wesley, 1984.

[2]Cohen P., David Day, Jefferson DeLisio, Michael Greenberg, Rick Kjeldsen, Daniel Suthers and Paul Berman. Management of Uncertainty in Medicine. International Journal of Approximate Reasoning, Spring, 1987.

3Davis R. and Buchanan B.G., Meta-level knowledge. In Buchanan B.C. and Shortliffe E.H. (Eds.) Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project, Reading, MA.: Addison-Wesley, 1984.

[4]Durfee E. and Lesser V. (1986) Incremental planning to control a blackboard-based problem solver. Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 58–64.

[5]Erman L.D., Hayes-Roth F., Lesser V. and Reddy D.R.The Hearsay-II speech understanding system: Integrating knowledge to resolve uncertainty. ACM Computing Survey, 12: 213–253. 1980.

[6]Hayes-Roth B. (1985) A blackboard architecture for control. Artificial Intelligence, vol. 26, pp. 251–321.

7Hayes-Roth F. and Lesser V. (1977) Focus of attention in the Hearsay-II speech understanding system. Proceedings of the Fifth International Joint Conference on Articial Intelligence. Boston, MA. pp. 27–35.

[8]Nii H.P.The blackboard model of problem solving. AI Magazine7(2): 38–54. 1986a.

[9]Nii H.P.Blackboard systems part two: Blackboard application systems. AI Magazine7(3): 82–106. 1986b.