Abstract
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.
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Notes
The chessboard model of the environment has been adopted in several AI applications. In machine learning, for example, different reinforcement learning (Sutton and Barto 1998) techniques such as Q-learning (Watkins 1989) are designed to tackle problems in which the world is represented as a grid and in which objects are represented as points inhabiting the sectors of this grid.
Just to cite one significant example, the Defense Advanced Research Projects Agency—the agency of the US Department of Defense that is responsible for the development of new technologies for military use—sponsored a robotics research project with the goal of developing an autonomous vehicle running on roads.
This article appears in a collection of works that report the cutting-edge research in fuzzy theory and soft computing. Within this context, the authors draw a demarcation between type one uncertainty and type two uncertainty, meaning by this stochastic uncertainty and deterministic uncertainty, respectively. Then, on the basis of a correspondence-like argumentation, they claim that “fuzzy logic has proven to be a very promising tool for dealing with type two of uncertainty” and they conclude: “Stochastic theory is only effective with type one uncertainty” (Solo and Gupta 2007, p. 257).
It is worth pointing out here that the issue we have raised above against the demarcation hypothesis is not relevant in this context. Indeed, we are concerned here with a mathematical system: the fact that it is deterministic can be stated on the basis of a formal analysis and does not need to be assessed empirically.
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Acknowledgments
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.
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Piscopo, C., Birattari, M. The Metaphysical Character of the Criticisms Raised Against the Use of Probability for Dealing with Uncertainty in Artificial Intelligence. Minds & Machines 18, 273–288 (2008). https://doi.org/10.1007/s11023-008-9097-3
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DOI: https://doi.org/10.1007/s11023-008-9097-3