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Adaptive Reasoning for Cooperative Agents

  • Conference paper
Applications of Declarative Programming and Knowledge Management (INAP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6547))

Abstract

Using explicit affirmation and explicit negation, whilst allowing for a third logic value of undefinedness, can be useful in situations where decisions have to be taken on the basis of scarce, ambiguous, or downright contradictory information. In a three-valued setting, we consider an agent that learns a definition for both the target concept and its opposite, considering positive and negative examples as instances of two disjoint classes. Explicit negation is used to represent the opposite concept, while default negation is used to ensure consistency and to handle exceptions to general rules. Exceptions are represented by examples covered by the definition for a concept that belong to the training set for the opposite concept.

One single agent exploring an environment may gather only so much information about it and that may not suffice to find the right explanations. In such case, a cooperative multi-agent strategy, where each agent explores a part of the environment and shares with the others its findings, might provide better results. We describe one such framework based on a distributed genetic algorithm enhanced by a Lamarckian operator for belief revision. The agents communicate their candidate explanations — coded as chromosomes of beliefs — by sharing them in a common pool. Another way of interpreting this communication is in the context of argumentation. In the process of taking all the arguments and trying to find a common ground or consensus we might have to change, or review, some of assumptions of each argument.

The resulting framework we present is a collaborative perspective of argumentation where arguments are put together at work in order to find the possible 2-valued consensus of opposing positions of learnt concepts in an evolutionary pool in order to find the “best” explanation to the observations.

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References

  1. Alferes, J.J., Brogi, A., Leite, J.A., Pereira, L.M.: Evolving logic programs. In: Flesca, S., et al. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, pp. 50–61. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  2. Alferes, J.J., Damásio, C.V., Pereira, L.M.: SLX - A top-down derivation procedure for programs with explicit negation. In: Bruynooghe, M. (ed.) Proc. Int. Symp. on Logic Programming. The MIT Press, Cambridge (1994)

    Google Scholar 

  3. Alferes, J.J., Pereira, L.M.: Reasoning with Logic Programming. LNCS (LNAI), vol. 1111. Springer, Heidelberg (1996)

    MATH  Google Scholar 

  4. http://www.psych.utoronto.ca/museum/baldwin.htm

  5. Baral, C., Gelfond, M., Rushton, J.N.: Probabilistic Reasoning With Answer Sets. In: Lifschitz, V., Niemelä, I. (eds.) LPNMR 2004. LNCS (LNAI), vol. 2923, pp. 21–33. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  6. Baral, C., Gelfond, M.: Logic programming and knowledge representation. Journal of Logic Programming 19/20, 73–148 (1994)

    Article  MATH  Google Scholar 

  7. Damásio, C.V., Nejdl, W., Pereira, L.M.: REVISE: An extended logic programming system for revising knowledge bases. In: Doyle, J., Sandewall, E., Torasso, P. (eds.) Knowledge Representation and Reasoning, pp. 607–618. Morgan Kaufmann, San Francisco (1994)

    Chapter  Google Scholar 

  8. Damásio, C.V., Pereira, L.M.: Default Negated Conclusions: Why Not? In: Herre, H., Dyckhoff, R., Schroeder-Heister, P. (eds.) ELP 1996. LNCS, vol. 1050, pp. 103–117. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  9. De Raedt, L., Bruynooghe, M.: Towards friendly concept-learners. In: Procs. of the 11th Intl. Joint Conf. on Artificial Intelligence, pp. 849–856. Morgan Kaufmann, San Francisco (1989)

    Google Scholar 

  10. De Raedt, L., Bruynooghe, M.: On negation and three-valued logic in interactive concept learning. In: Procs. of the 9th European Conf. on Artificial Intelligence (1990)

    Google Scholar 

  11. De Raedt, L., Bruynooghe, M.: Interactive concept learning and constructive induction by analogy. Machine Learning 8(2), 107–150 (1992)

    MATH  Google Scholar 

  12. Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artif. Intell. 77(2), 321–358 (1995)

    Article  MATH  Google Scholar 

  13. Esposito, F., Ferilli, S., Lamma, E., Mello, P., Milano, M., Riguzzi, F., Semeraro, G.: Cooperation of abduction and induction in logic programming. In: Flach, P.A., Kakas, A.C. (eds.) Abductive and Inductive Reasoning, Pure and Applied Logic. Kluwer, Dordrecht (1998)

    Google Scholar 

  14. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: Kowalski, R., Bowen, K.A. (eds.) Procs. of the 5th Int. Conf. on Logic Programming, pp. 1070–1080. MIT Press, Cambridge (1988)

    Google Scholar 

  15. Gordon, D., Perlis, D.: Explicitly biased generalization. Computational Intelligence 5(2), 67–81 (1989)

    Article  Google Scholar 

  16. Green, D.M., Swets, J.M.: Signal detection theory and psychophysics. John Wiley and Sons Inc., New York (1966) ISBN 0-471-32420-5

    Google Scholar 

  17. Greiner, R., Grove, A.J., Roth, D.: Learning active classifiers. In: Procs. of the Thirteenth Intl. Conf. on Machine Learning, ICML 1996 (1996)

    Google Scholar 

  18. Inoue, K.: Learning abductive and nonmonotonic logic programs. In: Flach, P.A., Kakas, A.C. (eds.) Abductive and Inductive Reasoning, Pure and Applied Logic. Kluwer, Dordrecht (1998)

    Google Scholar 

  19. Inoue, K., Kudoh, Y.: Learning extended logic programs. In: Procs. of the 15th Intl. Joint Conf. on Artificial Intelligence, pp. 176–181. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  20. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  21. Lamarck, J.B.: http://www.ucmp.berkeley.edu/history/lamarck.html

  22. Grefenstette, J.J.: Lamarckian learning in multi-agent environments (1991)

    Google Scholar 

  23. Lamma, E., Riguzzi, F., Pereira, L.M.: Learning in a three-valued setting. In: Procs. of the Fourth Intl. Workshop on Multistrategy Learning (1988)

    Google Scholar 

  24. Lamma, E., Pereira, L.M., Riguzzi, F.: Belief revision via lamarckian evolution. New Generation Computing 21(3), 247–275 (2003)

    Article  MATH  Google Scholar 

  25. Lamma, E., Riguzzi, F., Pereira, L.M.: Strategies in combined learning via logic programs. Machine Learning 38(1-2), 63–87 (2000)

    Article  MATH  Google Scholar 

  26. Lapointe, S., Matwin, S.: Sub-unification: A tool for efficient induction of recursive programs. In: Sleeman, D., Edwards, P. (eds.) Procs. of the 9th Intl. Workshop on Machine Learning, pp. 273–281. Morgan Kaufmann, San Francisco (1992)

    Google Scholar 

  27. Lavrač, N., Džeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood (1994)

    Google Scholar 

  28. Pazzani, M.J., Merz, C., Murphy, P., Ali, K., Hume, T., Brunk, C.: Reducing misclassification costs. In: Procs. of the Eleventh Intl. Conf. on Machine Learning (ML 1994), pp. 217–225 (1994)

    Google Scholar 

  29. Pereira, L.M., Pinto, A.M.: Revised stable models – A semantics for logic programs. In: Dias, G., et al. (eds.) EPIA 2005. LNCS (LNAI), vol. 3808, pp. 29–42. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  30. Pereira, L.M., Pinto, A.M.: Reductio ad absurdum argumentation in normal logic programs. In: Argumentation and Non-monotonic Reasoning (ArgNMR 2007) Workshop at LPNMR 2007, pp. 96–113 (2007)

    Google Scholar 

  31. Pereira, L.M., Pinto, A.M.: Approved Models for Normal Logic Programs. In: Dershowitz, N., Voronkov, A. (eds.) LPAR 2007. LNCS (LNAI), vol. 4790, pp. 454–468. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  32. Pinto, A.M.: Explorations in revised stable models — a new semantics for logic programs. Master’s thesis, Universidade Nova de Lisboa (February 2005)

    Google Scholar 

  33. Provost, F.J., Fawcett, T.: Analysis and visualization of classifier performance: Comparison under imprecise class and cost distribution. In: Procs. of the Third Intl. Conf. on Knowledge Discovery and Data Mining (KDD 1997). AAAI Press, Menlo Park (1997)

    Google Scholar 

  34. Quinlan, J.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)

    Google Scholar 

  35. Vere, S.A.: Induction of concepts in the predicate calculus. In: Procs. of the Fourth Intl. Joint Conf. on Artificial Intelligence (IJCAI 1975), pp. 281–287 (1975)

    Google Scholar 

  36. Whitley, D., Rana, S., Heckendorn, R.B.: The Island Model Genetic Algorithm: On Separability, Population Size and Convergence (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Centro de Inteligência Artificial - CENTRIA, Universidade Nova de Lisboa, Portugal

    Luís Moniz Pereira & Alexandre Miguel Pinto

Authors
  1. Luís Moniz Pereira
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  2. Alexandre Miguel Pinto
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Editors and Affiliations

  1. Departamento de Informática, Universidade de Évora and CENTRIA FCT/UNL, Portugal

    Salvador Abreu

  2. Universität Würzburg, Am Hubland, 97074, Würzburg, Germany

    Dietmar Seipel

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Pereira, L.M., Pinto, A.M. (2011). Adaptive Reasoning for Cooperative Agents. In: Abreu, S., Seipel, D. (eds) Applications of Declarative Programming and Knowledge Management. INAP 2009. Lecture Notes in Computer Science(), vol 6547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20589-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-20589-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20588-0

  • Online ISBN: 978-3-642-20589-7

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