The Metaphysical Character of the Criticisms Raised Against the Use of Probability for Dealing with Uncertainty in Artificial Intelligence
In artificial intelligence (AI), a number of criticisms were raised against the use of probability for dealing with uncertainty. All these criticisms, except what in this article we call the non-adequacy claim, have been eventually confuted. The non-adequacy claim is an exception because, unlike the other criticisms, it is exquisitely philosophical and, possibly for this reason, it was not discussed in the technical literature. A lack of clarity and understanding of this claim had a major impact on AI. Indeed, mostly leaning on this claim, some scientists developed an alternative research direction and, as a result, the AI community split in two schools: a probabilistic and an alternative one. In this article, we argue that the non-adequacy claim has a strongly metaphysical character and, as such, should not be accepted as a conclusive argument against the adequacy of probability.
KeywordsArtificial intelligence Probability Alternative approaches Randomness Uncertainty
Carlotta Piscopo acknowledges the support of a Training Site fellowship funded by the Improving Human Potential (IHP) programme of the Commission of the European Community, Grant HPMT-CT-2000-00032. Mauro Birattari acknowledges support from the fund for scientific research F.R.S.—FNRS of Belgium’s French Community, of which he is a Research Associate.
- Blair, B. (1999). Famous people: Then and now. Lotfi Zadeh. Creator of fuzzy sets. Azerbaijan International, December. Interview with Lotfi Zadeh.Google Scholar
- Brooks, R. A. (1991). Intelligence without reason. In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 569–595, Morgan Kaufmann Publisher, San Mateo, California.Google Scholar
- Buchanan, B. G., & Shortliffe, E. H. (Eds.). (1984). Rule-based expert systems. The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, MA, USA: Addison-Wesley.Google Scholar
- Cheeseman, P. (1985). In defense of probability. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp. 1002–1009, Morgan Kaufmann Publisher, San Mateo, CA, USA.Google Scholar
- Dubois, D., & Prade, H. (1987). Théorie des Possibilités (2nd ed.). Paris, France: Masson.Google Scholar
- Duda, R. O., Gaschnig, J., & Hart, P. E. (1979). Model design in the prospector consultant system for mineral exploration. In D. Michie (Ed.), Expert systems in the microelectronic age (pp. 153–167). Edinburgh, UK: Edinburgh University Press.Google Scholar
- Garvey, T. D., Lowrance, J. D., & Fischler, M. A. (1981). An inference technique for integrating knowledge from disparate sources. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence, pp. 319–325, Morgan Kaufmann Publisher, San Mateo, CA, USA.Google Scholar
- Marsaglia, G. (1995). Die Hard: A battery of tests for random number generators. http://stat.fsu.edu/∼geo/diehard.html.
- McCarthy, J., & Hayes, P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer & D. Michie (Eds.), Machine intelligence 4 (pp. 463–502). Edinburgh, UK: Edinburgh University Press.Google Scholar
- Newell, A., Shaw, J. C., & Simon, H. (1957). Empirical explorations with the logic theory machine: A case study in heuristics. In Proceedings of the Western Joint Computer Conference 15, pp. 218–239.Google Scholar
- Nilsson, N. J. (1984). Shakey the robot. Technical Report Technical Note 323, SRI AI Center, Menlo Park, CA, USA.Google Scholar
- Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Networks of plausible inference. San Mateo, CA, USA: Morgan Kaufmann.Google Scholar
- Popper, K. (1935). Logik der Forschung. Available as: The Logic of Scientific Discovery, London, United Kingdom: Routledge. 1999.Google Scholar
- Shortliffe, E. H. (1980). Consultation systems for physicians: The role of artificial intelligence techniques. In Proceedings of the Third National Conference of the Canadian Society for Computational Studies of Intelligence, pp. 1–11.Google Scholar
- Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning. An introduction. Cambridge, MA, USA: MIT Press.Google Scholar
- Watkins, C. J. C. H. (1989). Learning from Delayed Rewards. PhD thesis, King’s College, United Kingdom: Cambridge.Google Scholar
- Zadeh, L. A. (1981). Possibility theory and soft data analysis. In L. Cobb & R. M. Thrall (Eds.), Mathematical frontiers of the social and policy sciences (pp. 69–129). Boulder, CO, USA: Westview Press.Google Scholar