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.

[1]  Allen Newell,et al.  Chess-Playing Programs and the Problem of Complexity , 1958, IBM J. Res. Dev..

[2]  Masoud Nikravesh,et al.  Forging New Frontiers: Fuzzy Pioneers II , 2007 .

[3]  Rodney A. Brooks,et al.  Intelligence Without Reason , 1991, IJCAI.

[4]  A. Alexandrova.  The British Journal for the Philosophy of Science , 1965, Nature.

[5]  Patrick Billingsley,et al.  Probability and Measure. , 1986 .

[6]  Lotfi A. Zadeh,et al.  Toward a generalized theory of uncertainty (GTU) - an outline , 2005, GrC.

[7]  Peter C. Cheeseman,et al.  In Defense of Probability , 1985, IJCAI.

[8]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[9]  J. C. Shaw,et al.  Empirical explorations of the logic theory machine: a case study in heuristic , 1899, IRE-AIEE-ACM '57 (Western).

[10]  Donald Michie,et al.  Machine Intelligence 4 , 1970 .

[11]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[12]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[13]  Bruce G. Buchanan,et al.  Dendral and Meta-Dendral: Their Applications Dimension , 1978, Artif. Intell..

[14]  John Gaschnig,et al.  MODEL DESIGN IN THE PROSPECTOR CONSULTANT SYSTEM FOR MINERAL EXPLORATION , 1981 .

[15]  K. Popper.  Logik der Forschung : zur erkenntnistheorie der modernen naturwissenschaft , 1936 .

[16]  A. Shiryayev.  On Tables of Random Numbers , 1993 .

[17]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[18]  Peter Walley,et al.  Measures of Uncertainty in Expert Systems , 1996, Artif. Intell..

[19]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[20]  Madan M. Gupta,et al.  Uncertainty in Computational Perception and~Cognition , 2007 .

[21]  Nils J. Nilsson,et al.  Shakey the Robot , 1984 .

[22]  Bernadette Bouchon-Meunier,et al.  Théorie des possibilités , 2007 .

[23]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[24]  Antony Eagle,et al.  Randomness Is Unpredictability , 2005, The British Journal for the Philosophy of Science.

[25]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[26]  K. Popper,et al.  Logik der Forschung , 1935 .

[27]  Glenn Shafer,et al.  Non-additive probabilities in the work of Bernoulli and Lambert , 1978, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[28]  Didier Dubois,et al.  Bayesian conditioning in possibility theory , 1997, Fuzzy Sets Syst..

[29]  John McCarthy,et al.  SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE , 1987 .

[30]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[31]  Philippe Smets,et al.  The Transferable Belief Model , 1991, Artif. Intell..

[32]  Chris Watkins,et al.  Learning from delayed rewards , 1989 .

[33]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[34]  Lotfi A. Zadeh,et al.  Possibility theory and soft data analysis , 1996 .

[35]  Masoud Nikravesh,et al.  Evolution of fuzzy logic: from intelligent systems and computation to human mind , 2008, Soft Comput..

[36]  P. Walley.  Statistical Reasoning with Imprecise Probabilities , 1990 .

[37]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[38]  Peter Walley,et al.  Towards a unified theory of imprecise probability , 2000, Int. J. Approx. Reason..

[39]  Allen Newell,et al.  Empirical explorations with the logic theory machine: a case study in heuristics , 1995 .

[40]  Per Martin-Löf,et al.  The Definition of Random Sequences , 1966, Inf. Control..

[41]  Richard Bellman,et al.  Decision-making in fuzzy environment , 2012 .

[42]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[43]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[44]  T. Fine,et al.  The Emergence of Probability , 1976 .

[45]  Rodney A. Brooks,et al.  Elephants don't play chess , 1990, Robotics Auton. Syst..