Applied Intelligence

, Volume 13, Issue 3, pp 259–264 | Cite as

Defeasible Logic on an Embedded Microcontroller

  • Michael A. Covington


Defeasible logic is a system of reasoning in which rules have exceptions, and when rules conflict, the one that applies most specifically to the situation wins out. This paper reports a successful application of defeasible logic to the implementation of an embedded control system. The system was programmed in d-Prolog (a defeasible extension of Prolog), and the inferences were compiled into a truth table that was encoded on a low-end PIC microcontroller.

Advantages of defeasible logic include conciseness and correct handling of the passage of time. It is distinct from fuzzy logic and probabilistic logic, addressing a different set of problems.

microcontroller logic programming defeasible logic defaults embedded systems 


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  1. 1.
    D. Nute, "Basic defeasible logic," in Intensional Logics for Programming, edited by L. Fariñas del Cerro and M. Penttonen, Oxford University Press: Oxford, pp. 125–154, 1992.Google Scholar
  2. 2.
    G. Antoniou, Nonmonotonic Reasoning, MIT Press: Cambridge, Mass., 1997.Google Scholar
  3. 3.
    D. Nute, "Defeasible prolog," in Prolog Programming in Depth, 2nd ed., edited by M. Covington, D. Nute, and A. Vellino, Prentice-Hall: Upper Saddle River, N.J., pp. 345–405, 1997.Google Scholar
  4. 4.
    D. Nute, "d-Prolog: an implementation of defeasible logic in Prolog," in Non-Monotonic Extensions of Logic Programming: Theory, Implementation, and Applications, edited by J. Dix, L.M. Pereira, and T. Przymusinski, pp. 161–182. Research Report 17/96, Institut f¨ur Informatik, University of Koblenz-Landau, 1996.Google Scholar
  5. 5.
    J.L. Pollock, "How to reason defeasibly," Artificial Intelligence, vol. 57, pp. 1–42, 1992.Google Scholar
  6. 6.
    J.L. Pollock, "Architecture for an artificial agent that reasons defeasibly," United States Patent 5,706,406, January 6, 1998.Google Scholar
  7. 7.
    S. Hanks and D. McDermott, "Nonmonotonic logic and temporal projection," Artificial Intelligence, vol. 33, pp. 379–412, 1987.Google Scholar
  8. 8.
    E. Sandewall and Y. Shoham, "Non-monotonic temporal reasoning," in Handbook of Logic in Artificial Intelligence and Logic Programming, edited by D.M. Gabbay, C.J. Hogger, and J.A. Robinson, Clarendon Press: Oxford, vol. 4, pp. 439–498, 1995.Google Scholar
  9. 9.
    J. Bezdek, "Editorial: fuzzy models-what are they, and why?" in Fuzzy Logic Technology and Applications, edited by R.J. Marks II, IEEE: New York, pp. 3–7, 1992.Google Scholar
  10. 10.
    H. Surmann, A.P. Ungering, T. Kettner, and K. Goser, "What kind of hardware is necessary for a fuzzy rule based system?" in Proceedings, Third IEEE Conference on Fuzzy Systems, IEEE: New York, vol. 1, pp. 274–278, 1994.Google Scholar
  11. 11.
    R. Jager, H.B. Verbruggen, and P.M. Bruijn, "Demystification of fuzzy control," in Fuzzy Reasoning in Information, Decision and Control Systems, edited by S.G. Tzafestas and A.N. Venetsanopoulos, Kluwer: Dordrecht, pp. 165–197, 1994.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Michael A. Covington
    • 1
  1. 1.Artificial Intelligence CenterThe University of GeorgiaAthensUSA

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