, Volume 110, Issue 3, pp 357–379 | Cite as


  • Yao-Hua Tan


Currently there is hardly any connection between philosophy of science and Artificial Intelligence research. We argue that both fields can benefit from each other. As an example of this mutual benefit we discuss the relation between Inductive-Statistical Reasoning and Default Logic. One of the main topics in AI research is the study of common-sense reasoning with incomplete information. Default logic is especially developed to formalise this type of reasoning. We show that there is a striking resemblance between inductive-statistical reasoning and default logic. A central theme in the logical positivist study of inductive-statistical reasoning such as Hempel’s Criterion of Maximal Specificity turns out to be equally important in default logic. We also discuss to what extent the relevance of the results of Logical Positivism to AI research could contribute to a reevaluation of Logical Positivism in general.


Artificial Intelligence Logical Positivism Main Topic Incomplete Information Central Theme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Benthem, J. F. A. K. van: 1989, ‘Semantic Parallels in Natural Language and Computation’, in: H-D. Ebbinghaus, J. Fernandez-Prida, M. Carrido, D. Lascar and A. Rodriquez Artalejo (eds.), Logic Colloquium Granada 1987, North-Holland, Amsterdam, pp. 331–375.Google Scholar
  2. Benthem, J. F. A. K. van: 1990, ‘Inference, Methodology and Semantics’, in: A. Blynov and P. Bystrov (eds.), Festschrift for Vladimir Smirnov, Philosophical Institute, Academy of Science of the USSR, Moskow.Google Scholar
  3. Besnard, P.: 1989, An Introduction to Default Logic, Springer Verlag, Berlin.Google Scholar
  4. Buchanan, B. and E. Shortliffe: 1984, Rule-based Expertsystems: The MYCIN Experiments of the Stanford Heuristic Programming Project, Addison-Wesley, Reading, MA.Google Scholar
  5. Carnap, R.: 1948, Meaning and Necessity, Chicago University Press, Chicago.Google Scholar
  6. Etherington, D. W.: 1987, ‘Formalizing Nonmonotonic Reasoning Systems’, Artificial Intelligence 31.Google Scholar
  7. Etherington, D. W.: 1988, Reasoning with Incomplete Information, Pitman, London.Google Scholar
  8. Ginsberg, M. (ed.): 1987, Readings in Non-Monotonic Logic, Morgan Kaufman, Los Altos, CA.Google Scholar
  9. Halpern, J.: 1986, ‘Reasoning about Knowledge: an Overview’, Proc. of the First Conference about Theoretical Aspects of Reasoning about Knowledge, Morgan Kaufman, Los Altos, CA.Google Scholar
  10. Hempel, C. G.: 1965, ‘Aspects of Scientific Explanation’, in: C. G. Hempel, Aspects of Scientific Explanation and other Essays in the Philosophy of Science, The Free Press, New York.Google Scholar
  11. Hempel, C. G.: 1968, ‘Maximal Specificity and Law likeness in Probabilistic Explanation’, Philosophy of Science 35, 116–33.Google Scholar
  12. Hintikka, J.: 1962, Knowledge and Belief, Cornell University Press, Ithaca.Google Scholar
  13. Horty, J., R. Thomason, and D. Touretzky: 1987, A Skeptical Theory of Inheritance in Nonmonotonic Semantic Networks, Technical Report CMU-CS-87-175, Carnegie Mellon University, Pittsburgh.Google Scholar
  14. Humphreys, W.: 1968, ‘Statistical Ambiguity and Maximal Specificity’, Philosophy of Science 35, 112–5.Google Scholar
  15. Janssen, M. C.W. and Y. H. Tan: 1991, ‘Why Friedman’s Non-monotonic Reasoning Defies Hempel’s Covering Law Model’, Synthese 86, 255–84.Google Scholar
  16. Janssen, M. C. W. and Y. H. Tan: 1992, ‘Friedman’s Permanent Income Hypothesis as an Example of Diagnostic Reasoning’, Economics and Philosophy 8, 23–49.Google Scholar
  17. Konolige, K.: 1988, ‘On the Relation Between Default and Autoepistemic Logic’, Artificial Intelligence 35, 343–82.Google Scholar
  18. Kraus, S., D. Lehmann and M. Magidor: 1990, ‘Nonmonotonic Reasoning, Preferential Models and Cumulative Logics’, Artificial Intelligence 44, 167–207.Google Scholar
  19. Kuipers, T. A. F: 1978, Studies in Inductive Probability and Rational Expectation, Reidel, Dordrecht.Google Scholar
  20. Langley, P. and J. Zytkow: 1989, ‘Datadriven Approaches to Empirical Discovery’, Artificial Intelligence 13.Google Scholar
  21. Langley, P., J. Zytkow, H. Simon and G. Bradshaw: 1987, Scientific Discovery: Computational Explorations of the Creative Processes, MIT Press, Cambridge MA.Google Scholar
  22. Lewis, D.: 1969, Convention: A Philosophical Study, Harvard University Press, Harvard.Google Scholar
  23. Lewis, D.: 1973, Counterfactuals, Blackwell, Oxford.Google Scholar
  24. Lukaszewicz, W.: 1990, Non-Monotonic Reasoning – Formalization of Common Sense Reasoning, Series in Artificial Intelligence, Ellis Horwood, Chichester.Google Scholar
  25. Marek, W. and M. Truszczynski: 1993, Nonmonotonic Logic: Context-Dependent Reasoning, Springer-Verlag, Berlin.Google Scholar
  26. McCarthy, J.: 1980, ‘Circumscription – A Form of Non-monotonic Reasoning’, Artificial Intelligence 13, 27–39.Google Scholar
  27. Moore, R. C.: 1985, ‘Semantical Considerations on Nonmonotonic Logic’, Artiflcial Intelligence 25, 75–94.Google Scholar
  28. Poole, D.: 1990, ‘The Effect of Knowledge on Belief: Conditioning, Specificity and the Lottery Paradox in Default Reasoning’, Artificial Intelligence 49, 281–307.Google Scholar
  29. Prakken, H.: 1993, ‘An Argumentation Framework in Default Logic’, Annales of Mathematics and Artificial Intelligence 9, 91–131.Google Scholar
  30. Reiter, R.: 1980, ‘A Logic for Default Reasoning’, Artificial Intelligence 13, 81–132.Google Scholar
  31. Reiter, R.: 1987, ‘A Theory of Diagnosis from First Principles’, Artificial Intelligence 32, 57–96.Google Scholar
  32. Risch, V. and C. Schwind: 1994, Tableau-based Characterization and Theorem proving for Default Logic, Journal of Automated Reasoning 13, 223–242.Google Scholar
  33. Salmon, W. C.: 1990, Four Decades of Scientific Explanation, University of Minnesota Press, Minneapolis.Google Scholar
  34. Schaerf, M. and M. Cadoli: 1995, ‘Tractable Reasoning via Approximations’, Artificial Intelligence 74, 1–62.Google Scholar
  35. Shoham, Y.: 1988, Reasoning about Change. Time and Causation from the Standpoint of Artificial Intelligence, MIT Press, Cambridge MA.Google Scholar
  36. Stegmüller, W.: 1983, Erklärung, Begründung, Kausalität, Second edition, Springer Verlag, Berlin.Google Scholar
  37. Sterling, I. and E. Shapiro: 1986, The Art of Prolog, MIT Press, Cambridge MA.Google Scholar
  38. Suppe, F.: 1977, The Structure of Scientific Theories, second edition, University of Illinois Press, Urbana.Google Scholar
  39. Thagard, P.: 1988, Computational Philosophy of Science, MIT Press, Cambridge MA.Google Scholar
  40. Tan, Y. H.: 1988, ‘Explanations with Incomplete Information; a Problem for Hempel’s Theory about Causal Explanations’, in W. Callebaut and P. Mostert (eds.), Proceedings of the 9th Dutch Philosophy Conference, Eburon, Delft (in Dutch).Google Scholar
  41. Tan, Y. H.: 1991, ‘Non-monotonic Epistemic Aspects of Scientific Explanations’, Logique et Analyse 133-134, 197–220.Google Scholar
  42. Touretzky, D., Horty, J. and Thomason, R.: 1987, ‘A Clash of Intuitions: The Current State of Nonmonotonic Multiple Inheritance Systems’, Proceedings of the 10th International Joint Conference on Artificial Intelligence, IJCAI’87, 476–482.Google Scholar
  43. Veltman, F.: 1996, ‘Defaults in Update Semantics’, to appear in the Journal of Philosophical Logic.Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Yao-Hua Tan
    • 1
  1. 1.Erasmus University Research Institute for Decision and Information Systems (EURIDIS)Erasmus University RotterdamThe Netherlands

Personalised recommendations