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Human and Machine Perception 2 pp 137–149Cite as

Creations and Discoveries in Science: The Role of Abductive Reasoning

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Abstract

In different theoretical changes in theoretical systems we witness different kinds of discovery processes operating. Discovery methods are “data-driven”, “explanation-driven” (abductive), and “coherence-driven” (formed to overwhelm contradictions). Sometimes there is a mixture of such methods: for example, an hypothesis devoted to overcome a contradiction is found by abduction. When a contradiction (or an anomaly) arises, consistency can be restored by rejecting or modifying any assumption which contributes to the derivation of contradiction. Hence, the derivation of inconsistency contributes to the search for alternative, and possibly new hypotheses.

The aim is to emphasise the significance of abduction in order to illustrate the problem solving process and to propose a unified epistemological model of scientific discovery. The model first describes the different meanings of the word abduction (creative, selective, to the best explanation, “model-based”) in order to clarify their significance for epistemology and artificial intelligence. I will also illustrate abductive reasoning and its formal models in order to classify and analyse the different roles played by inconsistencies in different reasoning tasks. Many ways of “governing” inconsistencies are considered: from the methods activated in diagnostic settings and consistency-based models to the typical ones embedded in some forms of creative reasoning, from the interpretations in terms of conflicts and competitions to the actions performed on empirical and conceptual anomalies, from the question of generating inconsistencies by radical innovation to the connectionist treatment of coherence.

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References

  1. P. Thagard, Conceptual Revolutions, Princeton University Press, Princeton (1992).

    Google Scholar 

  2. K. Popper, Conjectures and Refutations. The Growth of Scientific Knowledge, Routledge and Kegan Paul, London (1963).

    Google Scholar 

  3. I. Lakatos, Proofs and Refutations. The Logic of Mathematical Discovery, Cambridge University Press, Cambridge (1976).

    Book  MATH  Google Scholar 

  4. W.V.O. Quine, Two dogmas of empiricism, Philosophical Review, 40, pp. 113–127 (1951). Also in: From a Logical Point of View, W.V.O. Quine, Hutchinson, London, pp. 20–46 (1953) (1961 2nd).

    Google Scholar 

  5. I. Lakatos, Falsification and the methodology of scientific research programs, in: Criticism and the Growth of Knowledge, I. Lakatos and A. Musgrave eds., Cambridge University Press, Cambridge, pp. 91–195 (1970).

    Google Scholar 

  6. I. Lakatos, History of science and its rational reconstructions, in: PSA 1970: In memory of Rudolf Carnap, R. Buck and R. S. Cohen eds., Reidel, Dordrecht (1971).

    Google Scholar 

  7. A. Anderson and N. Belnap, Entailment, Princeton University Press, Princeton (1975).

    MATH  Google Scholar 

  8. L. Magnani, Withdrawing hypotheses by negation as failure, in: Essays in Honor of Imre Toth, K. Chemla and S. Probst eds., forthcoming.

    Google Scholar 

  9. P. Thagard and C.P. Shelley, Abductive reasoning: logic, visual thinking, and coherence, in: Logic and Scientific Methods, M.L. Dalla Chiara, K. Doets, D. Mundici, and J. van Benthem eds., Kluwer, Dordrecht, pp. 413–427 (1997).

    Google Scholar 

  10. G. Lanzola, M. Stefanelli, G. Barosi, and L. Magnani, NEOANEMIA: A knowledge-based system emulating diagnostic reasoning, Computers and Biomedical Research, 23, pp. 560–582 (1990).

    Article  Google Scholar 

  11. M. Ramoni, M. Stefanelli, L. Magnani, and G. Barosi, An epistemological framework for medical knowledge-based systems, IEEE Transactions on Systems, Man, and Cybernetics, 22(6), pp. 1361–1375 (1992).

    Article  Google Scholar 

  12. L. Magnani, Abductive reasoning: philosophical and educational perspectives in medicine, in: Advanced models of cognition for medical training and practice, D.A. Evans and V.L. Patel eds., Springer, Berlin, pp. 21–41 (1992).

    Google Scholar 

  13. M. Stefanelli and M. Ramoni, Epistemological constraints on medical knowledge-based systems, in: Advanced Models of Cognition for Medical Training and Practice, D.A. Evans and V.L. Patel eds., Springer, Berlin, pp. 3–20 (1992).

    Google Scholar 

  14. I. Peng and I.A. Reggia, A probabilistic causal model for diagnostic problem solving I: integrating symbolic causal inference with numeric probabilistic inference, IEEE Transactions on Systems, Man, and Cybernetics, 17, pp. 146–162 (1987).

    Article  MATH  Google Scholar 

  15. I. Peng and I.A. Reggia, A probabilistic causal model for diagnostic problem solving II: diagnostic strategy, IEEE Transactions on Systems, Man, and Cybernetics, 17, pp. 395–406 (1987).

    Article  MATH  Google Scholar 

  16. H.E. Pople, On the mechanization of abductive logic, in: Proceedings of the International Joint Conference onArtifrcialIntelligence, 8, pp. 147–152 (1973).

    Google Scholar 

  17. J.A. Reggia, S.N. Dana, and Y.W. Pearl, Expert systems based on set covering model, International Journal on Man-Machine Studies, 19, pp. 443–460 (1983).

    Google Scholar 

  18. P. Thagard, Computational Philosophy of Science, The MIT Press, Cambridge, MA (1988).

    Google Scholar 

  19. J.R. Josephson, B. Chandrasekaran, J.W.jr Smith, and M.C. Tanner, Abduction by classification and assembly, in PSA 1986, vol. 1, Philosophy of Science Association, pp. 458–470 (1986).

    Google Scholar 

  20. J.R. Josephson and S.G. Josephson, Abductive Inference. Computation, Philosophy, Technology, Cambridge University Press, Cambridge (1994).

    Book  MATH  Google Scholar 

  21. L. Magnani, Epistémologie de l’invention scientifique, Communication & Cognition, 21, 3/4, pp. 273–291 (1988).

    Google Scholar 

  22. C.S. Peirce, Collected Papers, 8 vols., C. Harstone, P. Weiss, and A. Burks eds., Harvard University Press, Camdridge, MA, pp. 1931–1958.

    Google Scholar 

  23. L. Magnani, S. Civita, and G. Previde Massara, Visual cognition and cognitive modeling, in: Human and Machine Vision: Analogies and Divergences, V. Cantoni ed., New York, Plenum Press, pp. 229–243 (1994).

    Google Scholar 

  24. H.J. Holyoak and P. Thagard, Mental Leaps. Analogy in Creative Thought, The MIT Press, Cambridge, MA.

    Google Scholar 

  25. N.J. Nersessian, Reasoning from imagery and analogy in scientific concept formation, in: PSA 1988, vol 1, East Lansing: Philosophy of Science Association, A. Fine and J. Leplin eds., pp. 41–47 (1988).

    Google Scholar 

  26. N.J. Nersessian, Opening the black box: cognitive science and history of science. Technical Report GITCOGSCI 94/23, July. Cognitive Science Report Series. Atlanta: Georgia Institute of Technology. Partially published in Osiris 10, pp. 194–211 (1995).

    Article  Google Scholar 

  27. N. Nersessian, Constructive Modeling in Creating Scientific Understanding, Science & Education, 4, pp. 203–226 (1995).

    Article  Google Scholar 

  28. N. Nersessian, T.W. Griffith, and A. Goel, Constructive Modeling in Scientific Discovery, Technical Report, Georgia Institute of Technology, Atlanta, GA (1997).

    Google Scholar 

  29. T. Bylander, D. Allemang, M.C. Tanner, and J.R Josephson, The computational complexity of abduction, Artificial Intelligence, 49, pp. 25–60 (1991).

    Article  MathSciNet  MATH  Google Scholar 

  30. K. Konolige, Abduction versus closure in causal theories, Artificial Intelligence, 53, pp. 255–272 (1992).

    Article  MathSciNet  MATH  Google Scholar 

  31. H.J. Levesque, A knowledge level account of abduction, in: Proceedings IJCAI-89, Dedroit, MI, pp. 1061–1067 (1989).

    Google Scholar 

  32. R. Reiter, A theory of diagnosis from first principles, Artificial Intelligence, 32, pp. 57–95 (1987).

    Article  MathSciNet  MATH  Google Scholar 

  33. M. Shanahan, Prediction is deduction but explanation is abduction, in: Proceedings IJCAI-89, Detroit, MI, pp. 1140–1145 (1989).

    Google Scholar 

  34. R. Reiter and J. De Kleer, Foundations of assumption-based truth maintenance systems: preliminary report, in: Proceedings AAAI-87, Seattle, WA, pp. 183–188 (1987).

    Google Scholar 

  35. C. Boutilier and V. Becher, Abduction as belief revision, Artificial intelligence, 77, pp. 43–94 (1995).

    Article  MathSciNet  MATH  Google Scholar 

  36. C. Alchourrón, P. Gärdenfors, and P. Makinson, On the theory of logic change: partial meet functions for contractions and revision, Journal of Symbolic Logic, 50, pp. 510–530 (1985).

    Article  MathSciNet  MATH  Google Scholar 

  37. P. Gärdenfors, Knowledge in Flux, The MIT Press, Cambridge (1988).

    MATH  Google Scholar 

  38. I. Levi, For the Sake of the Argument. Ramsey Test Conditionals, Inductive Inference, and Nonmonotonic Reasoning, Cambridge University Press, Cambridge (1996).

    Book  MATH  Google Scholar 

  39. J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Mateo, CA (1988).

    Google Scholar 

  40. D. Poole, A logical framework for default reasoning, Artificial Intelligence, 36, pp. 27–47 (1988).

    Article  MathSciNet  MATH  Google Scholar 

  41. D. Poole, Representing diagnostic knowledge for probabilistic Horn abduction, in: Proceedings IJCAI91, Sydney, NSW, pp. 1129–1135 (1991).

    Google Scholar 

  42. J. de Kleer, A.K. Mackworth, and R. Reiter, Characterizing diagnoses, in: Proceedings AAAI-90, Boston, MA, pp. 324–330 (1990).

    Google Scholar 

  43. G. Brewka, Preferred subtheories: an extended logical framework for default reasoning, Proceedings IJCAI-89, Detroit, MI, pp. 1043–1048 (1989).

    Google Scholar 

  44. P. Gärdenfors ed., Belief Revision, Cambridge University Press, Cambridge (1992).

    MATH  Google Scholar 

  45. J. Doyle, A truth maintenance system, Artificial Intelligence, 12, pp. 231–272 (1979).

    Article  MathSciNet  Google Scholar 

  46. J. Doyle, Reason maintenance and belief revision: foundations versus coherence theories, in: Belief Revision, P. Gärdenfors ed., Cambridge University Press, Cambridge, pp. 29–51 (1992).

    Chapter  Google Scholar 

  47. W.V.O. Quine, Philosophy of Logic, Prentice-Hall, Englewood Cliffs, NJ (1979).

    Google Scholar 

  48. C.G. Hempel, Philosophy of Natural Science, Prentice-Hall, Englewood Cliffs, NJ (1966).

    Google Scholar 

  49. H. Katsuno and A. Mendelzon, On the difference between updating a knowledge base and revising it, in: Belief Revision, P. Gärdenfors ed., Cambridge University Press, Cambridge, pp. 183–203 (1992).

    Chapter  Google Scholar 

  50. C. Cross and R.H. Thomason, Conditionals and knowledge-base update, in: Belief Revision, P. Gärdenfors ed., Cambridge University Press, Cambridge, pp. 247–275 (1992).

    Chapter  Google Scholar 

  51. L. Darden, Theory Change in Science: Strategies from Mendelian Genetics, Oxford University Press, Oxford (1991).

    Google Scholar 

  52. L. Magnani, Ingegnerie della conoscenza. Introduzione alla filosofia computazionale, Marcos y Marcos, Milan (1997).

    Google Scholar 

  53. P. Thagard and K. Verbeurgt, Coherence as constraint satisfaction, Cognitive Science, 22(1), pp. 1–24 (1998).

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Philosophy Laboratory Department of Philosophy, University of Pavia, Pavia, I 27100, Italy

    Lorenzo Magnani

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  1. Lorenzo Magnani
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Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni  & Alessandra Setti  & 

  2. University of Palermo, Palermo, Italy

    Vito Di Gesù  & Domenico Tegolo  & 

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Magnani, L. (1999). Creations and Discoveries in Science: The Role of Abductive Reasoning. In: Cantoni, V., Di Gesù, V., Setti, A., Tegolo, D. (eds) Human and Machine Perception 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4809-6_13

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  • DOI: https://doi.org/10.1007/978-1-4615-4809-6_13

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7179-3

  • Online ISBN: 978-1-4615-4809-6

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