A Pragmatic Logic of Scientific Discovery

  • Jean Sallantin
  • Christopher Dartnell
  • Mohammad Afshar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)


To the best of our knowledge, this paper is the first attempt to formalise a pragmatic logic of scientific discovery in a manner such that it can be realised by scientists assisted by machines. Using Institution Agents, we define a dialectic process to manage contradiction. This allows autoepistemic Institution Agents to learn from a supervised teaching process. We present an industrial application in the field of Drug Discovery, applying our system in the prediction of pharmaco-kinetic properties (ADME-T) and adverse side effects of therapeutic drug molecules.


Normative System Deontic Logic Paraconsistent Logic Membership Query Dialectic Process 
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. 1.
    Popper, K.R.: Conjectures and Refutations: The Growth of Scientific Knowledge. Harper and Row (1963)Google Scholar
  2. 2.
    Lakatos, I.: Proofs and Refutations. Cambridge University Press, Cambridge (1976)MATHGoogle Scholar
  3. 3.
    da Costa, N.C.A, Beziau.: La logique paraconsistante. Le concept de preuve à la lumière de l’intelligence artificielle, 107–115 (1999)Google Scholar
  4. 4.
    da Costa, N.C.A.: Paraconsistent mathematics. In: Frontiers of paraconsistent logics. RSP research study press (2000)Google Scholar
  5. 5.
    da Costa, N.C.A.: Logiques classiques et non classiques: essai sur les fondements de la logique. Masson (1997)Google Scholar
  6. 6.
    Beziau, J.Y.: La logique paraconsistante. Logiques classiques et non classiques, essai sur les fondements de la logique (1997)Google Scholar
  7. 7.
    Dalla Pozza, C., Garola, C.: A pragmatic interpretation of intuitionistic propositional logic. Erkenntnis 43, 81–109 (1995)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Nakamatsu, K., Kato, T., Suzuki, A.: Basic ideas of defeasible deontic traffic signal control based on a paraconsistent logic program evalpsn. Advances in Intelligent Systems and Robotics (2003)Google Scholar
  9. 9.
    Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)Google Scholar
  10. 10.
    Angluin, D.: Queries revisited. Theoretical Computer Science 313, 175–194 (2004)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Angluin, D., Krikis: Learning from different teachers. Machine Learning 51, 137–163 (2003)Google Scholar
  12. 12.
    Afshar, M., Lanoue, A., Sallantin, J.: New directions: Multidimensionnal optimization in drug discovery. Comprehensive Medicinal Chemistry 2(4) (2006)Google Scholar
  13. 13.
    Nobrega, G.M.D., Sallantin, C.: A contradiction driven approach to theory information: Conceptual issues pragmatics in human learning, potentialities. Journal of the Brazilian Computer Society 9, 37–55 (2003)Google Scholar
  14. 14.
    Sallantin, J.: La découverte scientifique assistée par des agents rationnels. Revue des sciences et technologie de l’information, 15–30 (2003)Google Scholar
  15. 15.
    Dartnell, C., Sallantin, J.: Assisting scientific discovery with an adaptive problem solver. In: Hoffmann, A., Motoda, H., Scheffer, T. (eds.) DS 2005. LNCS, vol. 3735, pp. 99–112. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jean Sallantin
    • 1
  • Christopher Dartnell
    • 2
  • Mohammad Afshar
    • 3
  1. 1.LIRMM, UMR 5506MontpellierFrance
  2. 2.EURIWARECherbourg-OctevilleFrance
  3. 3.Ariana PharmaceuticalsParisFrance

Personalised recommendations