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Responses to “An AI view of the treatment of uncertainty” by Alessandro Saffiotti

Published online by Cambridge University Press:  07 July 2009

Dominic Clark ,
Jim Baldwin ,
Hamid Berenji ,
Paul Cohen ,
Didier Dubois ,
John Fox ,
John Lemmer ,
Henri Prade ,
David Spiegelhalter  and
Philippe Smets
...Show all authors
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The Knowledge Engineering Review , Volume 3 , Issue 1 , March 1988 , pp. 59 - 86
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Copyright © Cambridge University Press 1988

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References

Bonissone, P, 1987. “Reasoning plausible”, in: Encyclopedia of Artificial Intelligence, Shapiro, S C Wiley (Eds.).Google Scholar
Kanal, L N and Lemmer, J F, (Eds.), 1986. Uncertainty In Artificial Intelligence, North-Holland.Google Scholar
Mamdani, A, Efstathiou, J and Pang, D, 1985. “Inference under uncertainty”, in Expert Systems 85, Merry, M (Ed.), CUP.Google Scholar
Saffiotti, A, 1988. “An AI view of the teatment of Uncertainty”, Knowledge Engineering Review, 2 (2).Google Scholar
Baldwin, J F, 1985. Support Logic Programming, Proc. of the NATO Advanced Study Institute on Fuzzy Sets Theory and Applications, Louvain-La-Neuve, Belgium.Google Scholar
Baldwin, J F, 1986. “Support logic programming”, Fuzzy Sets Theory and Applications, Jones, A and Zimmermann, H J (Eds.), pp. 133–170, D Reidel Pub. Co.Google Scholar
Baldwin, J F, 1987. “Evidential support logic programming”, Fuzzy Sets and Systems, pp. 1–26.Google Scholar
Baldwin, J F, Martin, T P and Pilsworth, B W, 1987. Fril Manual, Equipu AIR Ltd, 184 Hotwell Rd, Bristol.Google Scholar
Baldwin, J F, 1988. “Computational models of uncertainty reasoning in expert systems”, to appear in: Journal of Computers and Mathematics with Applications.Google Scholar
Baldwin, J F, 1988. FRIL Support Logic Programming, ITRC report, University of Bristol.Google Scholar
Hempel, C G, 1968. “Maximum specificity and lawlikeness in probabilistic explanation”, Philosophy of Science, 35 116–133.Google Scholar
Miller, G A, Johnson-Laird, P N, 1976. Language and perception, Harvard Univ. Press.Google Scholar
Salmon, W C, 1984. Scientific Explanation and the Causal Structure of the World, Princeton Univ. Press.Google Scholar
Suppes, P, 1984. Probabilistic Metaphysics, Blackwell.Google Scholar
Zadeh, L A, 1978. “Fuzzy sets as a basis for a theory of possibility”, Fuzzy Sets and Systems 1 3–28.Google Scholar
Baldwin, J F, 1986. “Automated fuzzy and probabilistic inference”, Fuzzy Sets and Systems 18, 219–235.Google Scholar
Barnett, J, 1981. “Computational methods for a mathematical theory of evidence”, Proceedings of IJCAI 81 pp. 868–875.Google Scholar
Berenji, H R, 1987. An integrated approach to reasoning with uncertainty in artificial intelligence”, Proceedings of the second NASA Ames AI Forum, Palo Alto.Google Scholar
Berenji, H R, 1988. “Treatment of Uncertainty in Artificial Intelligence”, in: Machine Intelligence for aerospace robotics, (eds., Ewald, Heer and Henry, Lum), AIAA.Google Scholar
Bonissone, P P and Decker, K S, 1986. “Selecting unoertainty calculi and granularity: an experiment in trading-off precision and complexity”, Uncertainty in AI, Kanal, and Lemmer, (Eds.), pp. 217–247, North-Holland.Google Scholar
Bonissone, P, 1987. Summarizing and propagating uncertain information with triangular norms”, International Journal of Approximate Reasoning, 1(1) pp. 71–101, North Holland Publishing Company.Google Scholar
Bonissone, P P, Gans, S S and Decker, K S, 1987. RUM: a layered architecture for reasoning with uncertainty”, IJCAI-87, Milan, Italy.Google Scholar
Carnap, R, 1962. Logical Foundations of Probability, University of Chicago Press, Chicago, Illinois.Google Scholar
Cheesemen, P, 1988. “An inquiry into computer understanding”, the Computational Intelligence 4, pp. 58–66.Google Scholar
Cheeseman, P, 1988. In defense of an inquiry to computer understanding”, the Computational Intelligence 4, pp. 129–142.Google Scholar
Cheeseman, P, 1986. “Probabilistic vs Fuzzy reasoning”, in: Uncertainty in AI, Kanal and Lemmer (Eds.).Google Scholar
Cheeseman, P, 1985. “In defense of probability”, Proceedings of IJCAI 85, pp. 1002–1009.Google Scholar
Dubois, D and Prade, H, 1985. “Combination and propagation of certainty with belief functions – a reexamination”, Proceedings of IJCAI-85, Los Angeles, CA.Google Scholar
Fox, J, 1986. “Three arguments for extending the framework of probability”, in: Kanal, L N and Lemmer, J F (Eds.), Uncertainty in Artificial Intelligence, Amsterdam: North Holland.Google Scholar
Gordon, J and Shortliffe, E, 1983. “The Dempster-Shafer theory of evidence”, in: Rule-Based Systems, Buchanan, B and Shortliffe, E (Eds.), Menlo Park: Addison-Wesley.Google Scholar
Heckerman, D, 1985. “A probabilistic interpretation for MYCIN's certainty factors”, in: Proceedings of AAAI Workshop on Uncertainty and Probability in AI, Los Angeles, CA, pp. 9–20.Google Scholar
Kong, A, 1986. Multivariate Belief Functions and Graphical Models, Research report S-107, Harvard University.Google Scholar
Klir, G J and Folger, T A, 1988. Fuzzy Sets, Uncertainty, and Information, Prentice Hall.Google Scholar
Larkin, L I, 1985. “A fuzzy logic controller for aircraft flight control”, Industrial Applications of Fuzzy Control, Sugeno (Ed.).Google Scholar
Lindey, D V, 1971. “Making decisions”, John Wiley, London.Google Scholar
Lindley, D V, 1987. The probability approach to the treatment of uncertainty in artificial intelligence and expert Systems”, Statistical Science.Google Scholar
Ostergaard, J J, 1977. “Fuzzy logic control of a heat exchanger process”, in: Fuzzy Automata and Decision Processes, North-Holland.Google Scholar
Prade, Henry, 1985. “A computational approach to approximate and plausible reasoning with applications to expert Systems”, IEEE Transactions on Pattern Analysis and Machine Intelligence PAM-7(3).Google Scholar
Pearl, Judea, 1986. “Fusion, propagation, and structuring in belief networks”, Artificial Intelligence 29 pp. 241–288.Google Scholar
Ruspini, E, 1986. Logical Foundation of evidential reasoning, SRI Tech, note 408.Google Scholar
Shafer, G, 1987. “Probability judgment in artificial intelligence expert Systems”, Statistical Science.Google Scholar
Shafer, G, Shenoy, P P and Mellouli, K, 1987. “Propagating belief functions in qualitative markov trees”, International Journal of Approximate Reasoning 1 pp. 349–400.Google Scholar
Shenoy, P P and Shafter, G, 1986. “Propagating belief functions with local computations”, IEEE Expert, pp. 43–52.Google Scholar
ShortliffeE, H E, H, 1976. “Computer based medical consultations: MYCIN”, American Elsevier, New York.Google Scholar
Thompson, T R, 1985. “Parallel formulation of evidential reasoning theories”, Proceedings of IJCAI-85, Los Angeles, CA.Google Scholar
Yager, R, 1988. “Responses to Peter Cheeseman's An Inquiry into Computer Understanding”, Computational Intelligence, pp. 125.Google Scholar
Zadeh, L A, 1986. “Is probability theory sufficient for dealing with uncertainty in AI: a negative view”, in: Uncertainty in AI, (Canal, and Lemmer, (Eds.), North Holland.Google Scholar
Clancey, William J, 1984. “Acquiring, representing, and evaluating a competence model of diagnosis”, KSL Memo 84–2, Stanford University, 02 1984. To appear in Contributions to the Nature of Expertise, Chi, Glaser and Farr, (Eds.), in preparation.Google Scholar
Cohen, Paul R, 1987. “The control of reasoning under uncertainty: a discussion of some programs”, The Knowledge Engineering Review 2(1).Google Scholar
Cohen, Paul R, 1987. “Managing uncertainty”, in: The Third Conference on Artificial Intelligence Applications,UMassCS,Orlando, Florida.Google Scholar
Cohen, Paul R, 1987. “Steps toward programs that manage uncertainty”, in: Third Workshop on Uncertainty in Artificial Intelligence, pp. 372–379, American Association for Artificial Intelligence.Google Scholar
Cohen, Paul R and Day, David S, 1988. The Centrality of Autonomous Agents in Theories of Action Under Uncertainty, EKSL Technical Report, University of Massachusetts, 01 1988. To appear in the International Journal for Approximate Reasoning.Google Scholar
Cohen, Paul R, Day, David S, Delisio, Jeff, Greenberg, Michael, Kjeldsen, Rick, Suthers, Dan and Berman, Paul, 1987. “Management of uncertainty in medicine”, International Journal of Approximate Reasoning 1(1) pp. 103–116.Google Scholar
Cohen, Paul R, Greenberg, Michael and Delisio, Jeff, 1987. “MU: A development environment for prospective reasoning Systems, in: Proceedings of the Sixth National Conference on Artifidal Intelligence, pp. 783–788, Seattle, Washington,July 1987.Google Scholar
Erman, L and Lesser, V R, 1975. “A multi-level organization for problem solving using many diverse, cooperating sources of knowledge”, in: Proceedings of the International Joint Conference on Artifidal Intelligence, 1975.Google Scholar
Erman, L D, Hayes-Roth, F, Lesser, V R and Reddy, D R, 1980. “The HEARSAY-II speech understanding System: Integrating knowledge to resolve uncertainty”, Computing Surveys 12 pp. 213–253.Google Scholar
Hanks, Steve, 1987. “Temporal reasoning about uncertain worlds”, in: Third Workshop on Uncertainty in Artificial Intelligence, pp. 114–122, American Association for Artificial Intelligence.Google Scholar
Hayes-Roth, F, 1985. “A blackboard architecture for control”, Artificial Intelligence, 26 pp. 251–321.Google Scholar
Howe, Adele and Cohen, Paul, 1986. A Typology for Constructing Decisions, Department of Computer and Information Science 86–14, University of Massachusetts.Google Scholar
Tong, Richard M and Appelbaum, Lee A, 1987. “Problem structure and evidential reasoning”, in: Third Workshop on Uncertainty in Artificial Intelligence, pp. 313–319, American Association for Artificial Intelligence.Google Scholar
Wellman, Michael P, 1987. “Qualitative probabilistic networks for planning under uncertainty”, in: Uncertainty in Artificial Intelligence, John F Lemmer (Ed.).Google Scholar
Sombé, Léa (Besnard, P, Cordier, M O, Dubois, D, Fariñas del Cerro, L, Froidevaux, C, Moinard, Y, Prade, H, Schwind, C and Siegel, P) (1988). “Inférences non-classiques en intelligence artificielle-Ebauche de comparaisons sur un exemple”, Actes 2émes Journées Nationales du P.R.C.-G.R.E.C.O. Intelligence Artificielle, Toulouse, 14–15 Mars, 1988, Teknéa, Marseille, pp. 137–230.Google Scholar
Calabrese, P, 1987. “An algebraic synthesis of the foundations of logic and probability”, Information Sciences 42 pp. 187–237.Google Scholar
Chatalic, P, Dubois, D and Prade, H, 1987. “A System for handling relational dependencies in approximate reasoning”, Proc. 3rd Inter. Conf. on Expert Systems, London, 06 2–4, 1987, 495–502.Google Scholar
Dubois, D and Prade, H, 1982. “On several representations of an uncertain body of evidence”, in: Fuzzy Information and Decision Processes, Gupta, M M and Sanchez, E, (Eds.), North-Holland, pp. 167–181.Google Scholar
Dubois, D and Prade, H, 1988. Théorie des Possibilités. Applications à la Représentation des Connaissances en Informatique, Masson, Paris, 1985, (2nd revised and augmented edition, 1987). English translation: Possibility Theory. An Approach to the Computerized Processing of Uncertainty, Plenum Press, New York, 1988.Google Scholar
Dubois, D and Prade, H, 1986. “A set-theoretic view of belief functions: logical operations and approximation by fuzzy sets”, Inter. J. General Systems 12 pp. 193–226.Google Scholar
Dubois, D and Prade, H, 1987. “Necessity measures and the resolution principle”. IEEE Trans. on Systems, Mon and Cybernetics 17 pp. 474–478.Google Scholar
Dubois, D and Prade, H, 1988. “An introduction to possibilistic and fuzzy logics (with discussions and a reply)”, in: Non-Standard Logics for Automated Reasoning (Mamdani, E H, Smets, P, Dubois, D, Prade, H, Eds.), Academic Press, New York, pp. 287–326.Google Scholar
Fine, T, 1973. Theories of Probability, Academic Press, New York.Google Scholar
Kyburg, H, 1987. “Bayesian and non-Bayesian evidential updating”, Artificial Intelligence 31 pp. 271–293.Google Scholar
Pearl, J, 1986. “Fusion, propagation and structuring in belief networks”, Artificial Intelligence 29 pp. 241–288.Google Scholar
Quinlan, J R, 1983. “INFERNO: a cautious approach to uncertain inference”, The Computer Journal 26 pp. 255–269.Google Scholar
Reiter, R, 1980. “A logic of default reasoning”, Artificial Intelligence 13 pp. 81–132.Google Scholar
Zadeh, L A, 1978. “Fuzzy sets as a basis for a theory of possibility”, Fuzzy Sets and Systems 1 pp. 3–28.Google Scholar
Zadeh, L A, 1979. “A theory of approximate reasoning”, in: Machine Intelligence (Hayes, J E, Michie, D, Mikulich, L I, (Eds.), Elsevier, NY Volume 9 pp. 149–194.Google Scholar
Cooper, G F, 1984. NESTOR: A Computer-based Medical Diagnostic Aid that Integrates Causal and Probabilistic Knowledge, PhD, Th., Computer Science Department, Stanford University, Rep. No. STANCS-84–48.Google Scholar
Heckerman, D, 1986. “Probabilistic interpretations for MYCIN's certainty factors”, in: Uncertainty in Artificial Intelligence, Kanal, and Lemmer, (Eds.), North-Holland.Google Scholar
Lemmer, J F, 1986. “Confidence factors, empiricism and the Dempster-Shafer theory of evidence”, in: Uncertainty in Artificial Intelligence, Kanal, and Lemmer, (Eds.), North-Holland.Google Scholar
Pearl, J, 1986. “A constraint-propagation approach to probabilistic reasoning”, in: Uncertainty in Artificial Intelligence, Kanal, and Lemmer, (Eds.), North-Holland.Google Scholar
Shafer, G. 1976. A Mathematical Theory of Evidence, Princeton University Press.Google Scholar
Spiegelhalter, D J, 1986. “Probabilistic reasoning in predictive expert Systems”, in: Uncertainty in Artificial Intelligence, Kanal, and Lemmer, (Eds.), North-Holland.Google Scholar
Lauritzen, S L and Spiegelhalter, D J, 1988. “Local computations with probabilites on graphical structures and their application to expert Systems (with discussion)”. J. Roy. Statist. Soc., B, 50 (to appear).Google Scholar
Spiegelhalter, D L, 1986. “Probabilistic reasoning in predictive expert Systems”, in: Uncertainty in Artificial Intelligence, Kanal, L N and Lemmer, J (Eds.), North Holland, Amsterdam, pp. 47–68.Google Scholar
Pearl, J, 1987. “Do we need higher-order probabilities and, if so, what do they mean?” Proceedings of Third AAAI Workshop on Uncertainty in Artificial Intelligence, Seattle, pp. 47–60.Google Scholar
Good, I J, 1983. Good thinking. The foundation of probability and its applications. Univ. Minnesota Press, Minneapolis, Min.Google Scholar
Smets, Ph, 1988a. “Belief functions”, in: Smets, Ph, Mamdani, A, Dubois, D and Prade, H (Eds.). Non standard logics for automated reasoning, Academic Press, London pp. 253–286.Google Scholar
Smets, Ph, 1988b. “The combination of evidence in the transferable belief model” (submitted for publication).Google Scholar
Smets, Ph, 1988c. “The transferable belief model: comparison with Bayesian models”, (submitted for publication).Google Scholar
Baldwin, J F, 1987. SLOP—A System for Support Logic Programming, ITRC Research Report, University of Bristol, England.Google Scholar
Baldwin, J F, Martin, T P and Pilswood, B W, 1987. “The implementation of FPROLOG—A Fuzzy Prolog interpreter”, Fuzzy Sets and Systems 23 119–129.Google Scholar
Bonissone, P P, 1983. “A survey of uncertainty representation in expert systems”, Proc. Second Workshop of the North-American Fuzzy Information Processing Society, Schenectady, N.Y., General Electric Corporate Research and Development.Google Scholar
Cheeseman, P, 1985. “In defence of probability”, Proc. Int Joint Conference on Artificial IntelligenceLos Angeles, 1002–1009.Google Scholar
Cohen, P, 1985. Heuristic Reasoning about Uncertainty: An Artificial Intelligence Approach, Boston: Pitman.Google Scholar
Dubois, D and Prade, H, 1985. Théorie des Possibilités. Applications a la Representation des Connaissances en Informatique, Paris: Masson.Google Scholar
Dempster, A, 1967. “Upper and lower probabilities induced by a multivalued mapping”, Annals of Mathematical Statistics 38 325–339.Google Scholar
Fox, J, 1986. “Three arguments for extending the framework of probability”, in: Uncertainty in Artificial Intelligence, Kanal, L N and Lemmer, J F (Eds.), Amsterdam: Elsevier Science Publishers B.V.Google Scholar
Hinde, C J, 1986. “Fuzzy Prolog”, J. of Man-Machine Studies 24 569–595.Google Scholar
Kanai, N and Ishizuka, M, 1986. “Prolog-ELF incorporating fuzzy logic”, Translations of Information Processing Society of Japan 27 4.Google Scholar
Lee, R C T, 1978. “Fuzzy logic and the resolution principle”, J. ACM 19 109–119.Google Scholar
Goodman, I R and Nguyen, H T, 1989. Uncertainty Models for Knowledge-Based Systems, Amsterdam: North-Holland Publishing Co.Google Scholar
Mukaidono, M, Shen, Z and Ding, L, 1987. “Fuzzy Prolog”, Congress Reprints of the 2nd IFSA Congress 7 452–455.Google Scholar
Nilsson, N, 1984. Probabilistic Logic, SRI Tech. Note 321, Menlo Park, CA.Google Scholar
Orlov, A I, 1980. Problems of Optimization and Fuzzy Variables, Moscow: Znanie.Google Scholar
Prade, H and Negoita, C V, 1986. Fuzzy Logic in Knowledge Engineering, Rhineland: Verlag TUV.Google Scholar
Preprints of the Second Congress of the International Fuzzy Systems Association, Tokyo, Japan, 1987.Google Scholar
Ruspini, E H, 1987. “Semantic modeling of imprecise and uncertain knowledge”, The Analysis of Fuzzy Information II, Bezdek, J (Ed.), Boca Raton, FL.Google Scholar
Shafer, G, 1976. A Mathematical Theory of Evidence, Princeton: Princeton University Press.Google Scholar
Spiegelhalter, D, 1985. “A statistical view of uncertainty in expert systems”, in: Proc. of Workshop on AI and Statistics, Gale, W and Pregibon, D (Eds.), New York: Addison Wesley.Google Scholar
Sugeno, M, 1985. Industrial Applications of Fuzzy Control, The Netherlands: Elsevier Science Publishers B.V.Google Scholar
Togai, M and Watanabe, H, 1973. “Expert systems on a chip: An engine for real-time approximate reasoning”, IEEE Trans. on Systems, Man and Cybernetics SMC-3 28–44.Google Scholar
Van Emden, M H, 1986. “Quantitative deduction and its fixpoint theory”, J. of Logic Programming 1 37–53.Google Scholar
Watanabe, H and Dettloff, W, 1987. “Fuzzy logic inference processor for real time control: A second generation full custom design”, Proc. 21st Asilomar Conference on Signals, Systems and Computers,Asilomar, CA.Google Scholar
Yager, R R, 1985. “Reasoning with uncertainty for expert systems”, Proc. 9th Int. Joint Conference on Artificial Intelligence,Los Angeles, 1295–1297.Google Scholar
Zadeh, L A, 1979. “Fuzzy sets and information granularity”, in: Advances in Fuzzy Set Theory and Applications, Gupta, M, Ragade, R and Yager, R (Eds.), Amsterdam: North-Holland Publishing Co., 3–18.Google Scholar
Zadeh, L A, 1981. “Possibility theory and soft data analysis”, in: Mathematical Frontiers of the Social and Policy Sciences, Cobb, L and Thrall, R M (Eds.), Boulder, CO: Westview Press, 69–129.Google Scholar
Zadeh, L A, 1983. “The role of fuzzy logic in the management of uncertainty in expert systems”, Fuzzy Sets and Systems 11 199–227.Google Scholar
Zadeh, L A, 1986. “Is probability theory sufficient for dealing with uncertainty in AI: A negative view”, in: Uncertainty in Artificial Intelligence, Kanal, L N and Lemmer, J F (Eds.), Amsterdam: North-Holland Publishing Co., 103–116.Google Scholar
Cheesemen, P, 1985. “In defence of probability”, Proceedings of the international Joint Conference on Artificial Intelligence,Los Angeles, 1002–1009.Google Scholar
Fox, J, 1983. “Formal and knowledge based methods in decision technology”, Proc. 9th conference on Subjective Probability, Utility and Decision Making,Groningen, The Netherlands,August 1983. Extended version in Acta Psychologica, 1984.Google Scholar
Fox, J, 1986. “Three arguments for extending the framework of probability”, in: Kanal, L N and Lemmer, J F (Eds.), Uncertainty in Artificial Intelligence, Amsterdam: North Holland.Google Scholar
Fox, J, O'Neil, M, Glowinski, A J and Clark, D A, 1988. “Decision making as a logical process”, Proceedings of Expert Systems '88 Brighton. Cambridge University Press 1988.Google Scholar
Mamdani, E H and Efstathiou, H J, 1985. “Higher-order logics for handling uncertainty in expert systems”, Int. J. Man-Machine Studies 22 283–293.Google Scholar