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The control of reasoning under uncertainty: A discussion of some programs*

Published online by Cambridge University Press:  07 July 2009

Paul R. Cohen
Paul R. Cohen
Affiliation:
Department of Computer and Information Science, University of Massachusetts, Amherst, MA 01003†
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Abstract

This paper proposes that managing uncertainty is a control problem, a task for the control component of AI systems that decides what to do next. This view emphasizes the process of planning and executing sequences of actions that simultaneously satisfy domain goals and minimize uncertainty. The paper reviews AI systems that manage uncertainty by control. It is not an exhaustive survey, but rather illustrates issues in managing uncertainty with selected AI programs.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 2 , Issue 1 , March 1987 , pp. 5 - 25
Copyright
Copyright © Cambridge University Press 1987

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References

8 Structured bibliography

[1]Bonisonne, P. (1985) Reasoning with uncertainty in expert systems. International Journal of Man-Machine Studies, 22: 3.Google Scholar
[2]Cohen, P. (1985) Heuristic reasoning about uncertainty: an artificial intelligence approach. London: Pitman.Google Scholar
[3]Cohen, P. and Gruber, T. (1985) Reasoning about uncertainty: A knowledge representation perspective. In John Fox (Ed.) Pergamon Infotech State of the Art Report.Google Scholar
[4]Cohen, 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.Google Scholar
[5]Duda, 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.Google Scholar
[6]Gordon, 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.Google Scholar
[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.Google Scholar
[8]Lowrance, 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.Google Scholar
[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.CrossRefGoogle ScholarPubMed
[10]Quinlan, J.R. (1983) INFERNO: A cautious approach to uncertain inference. Computer Journal, vol. 26.Google Scholar
[11]Shafer, G. (1976) A mathematical theory of evidence. Princeton: Princeton University Press.CrossRefGoogle Scholar
[12]Shafer, G. (1984) Probability judgment in artificial intelligence and expert systems. University of Kansas Working Paper 165, Lawrence School of Business, Lawrence, Kansas.Google Scholar
[13]Shafer, G. and Tversky, A., (1986) Languages and designs for probability judgment. Cognitive Science, in press.CrossRefGoogle Scholar
[14]Shortliffe, 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.Google Scholar
[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.Google Scholar
[16]Szolovits, P. and Pauker, S.G. (1978) Categorical and Probabilistic Reasoning in Medical Diagnosis. Artificial Intelligence, vol. 11, pp. 115–144.CrossRefGoogle Scholar
[1]Chandrasakeran, B. (1986) Generic tasks in knowledge-based reasoning: high-level building blocks for expert system design. IEEE Expert, Fall, pp. 23–30.CrossRefGoogle Scholar
[2]Bylander, T. and Mittal, S.CSRL: A language for classificatory problem solving and uncertainty handling. AI Magazine. 7(3): 66–77, 1986.Google Scholar
[3]Clancey, W.J.Heuristic Classification. Artificial Intelligence. 27: 289–350, 1985.CrossRefGoogle Scholar
[4]Clancey, W.J.From GUIDON to NEOMYCIN and HERACLES in twenty short lessons. AI Magazine. 7(3): 40–60, 1986.Google Scholar
[5]Cohen, P.R., Greenberg, M., DeLisio, J. (1987) MU: A development environment for prospective reasoning systems. AAAI-87, 07, 1987, Seattle, WA.Google Scholar
[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.CrossRefGoogle Scholar
[7]Gruber, 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.Google Scholar
[8]Hayes-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.Google Scholar
[9]Kahn, 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.Google Scholar
[10]Marcus, 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.Google Scholar
[1]Cohen, P.R. and Feigenbaum, E.A. (1982) The Handbook of Artificial Intelligence, 3, Reading, MA: Addison-Wesley.Google Scholar
[2]Fikes, R., Hart, P. and Nilsson, N.Learning and executing generalized robot plans. Artificial Intelligence, 3(4): 251–288. 1972.CrossRefGoogle Scholar
[3]Fox, John (1985) Knowledge decision making and uncertainty. Workshop on Artificial Intelligence and Statistics, Bell Laboratories.Google Scholar
[4]Howard, 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.Google Scholar
[5]Howe, A. and Cohen, P.R. 1986. A typology for constructing decisions. Proceedings of IEEE Conference on Computers and Communications. Phoenix, AZ, 02, 1987.Google Scholar
[6]Raiffa, H.Decision Analysis: Introductory Lectures on Choices Under Uncertainty. Reading, MA: Addison-Wesley. 1970.Google Scholar
[7]Sacerdoti, E. (1979) Problem solving tactics. Proceedings of the Sixth international Joint Conference on Artificial Intelligence, pp. 1077–1085.Google Scholar
[8]Waltz, 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.Google Scholar
[1]Clancey, 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.Google Scholar
[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.CrossRefGoogle Scholar
[3]Davis, 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.Google Scholar
[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.Google Scholar
[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.CrossRefGoogle Scholar
[6]Hayes-Roth, B. (1985) A blackboard architecture for control. Artificial Intelligence, vol. 26, pp. 251–321.CrossRefGoogle Scholar
[7]Hayes-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.Google Scholar
[8]Nii, H.P.The blackboard model of problem solving. AI Magazine 7(2): 38–54. 1986a.Google Scholar
[9]Nii, H.P.Blackboard systems part two: Blackboard application systems. AI Magazine 7(3): 82–106. 1986b.Google Scholar