Advertisement

Belief Revision by Lamarckian Evolution

  • Evelina Lamma
  • Luís MonizPereira
  • Fabrizio Riguzzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

Abstract

We propose a multi-agent genetic algorithm to accomplish belief revision. The algorithm implements a new evolutionary strategy resulting from a combination of Darwinian and Lamarckian approaches. Besides encompassing the Darwinian operators of selection, mutation and crossover, it comprises a Lamarckian operator that mutates the genes in a chromosome that code for the believed assumptions. These self mutations are performed as a consequence of the chromosome phenotype’s experience obtained while solving a belief revision problem. They are directed by a belief revision procedure which relies on tracing the logical derivations leading to inconsistency of belief, so as to remove the latter’s support on the gene coded assumptions, by mutating the genes.

Keywords

Genetic Algorithm Logic Program Crossover Operator Belief Revision Integrity Constraint 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    T.M. Mitchell. Machine Learning. McGraw Hill, 1997.Google Scholar
  2. 2.
    Erick Cantú-Paz. A survey of parallel genetic algorithms.Google Scholar
  3. 3.
    C.V. Damásio, L.M. Pereira, and M. Schroeder. REVISE: Logic programming and diagnosis. In Proceedings of Logic-Programming and Non-Monotonic Reasoning, LPNMR’97, volume 1265 of LNAI, Germany, 1997. Springer-Verlag.Google Scholar
  4. 4.
    J.J. Alferes, L.M. Pereira, and T.C. Przymusinski. “Classical” negation in non-monotonic reasoning and logic programming. Journal of Automated Reasoning, 20:107–142, 1998.MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    A. Van Gelder, K.A. Ross, and J.S. Schlipf. The well-founded semantics for general logic programs. Journal of the ACM, 38(3):620–650, 1991.MathSciNetzbMATHGoogle Scholar
  6. 6.
    L.M. Pereira, C.V. Damásio, and J.J. Alferes. Diagnosis and debugging as contradiction removal. In L.M. Pereira and A. Nerode, editors, Proceedings of the 2nd International Workshop on Logic Programming and Non-monotonic Reasoning, pages 316–330. MIT Press, 1993.Google Scholar
  7. 7.
    E. Lamma, L.M. Pereira, and F. Riguzzi. Multi-agent logic aided lamarckian learning. Technical Report DEIS-LIA-00-004, Dipartimento di Elettronica, Informatica e Sistemistica, University of Bologna (Italy), 2000. LIA Series no. 44.Google Scholar
  8. 8.
    F. Brglez, P. Pownall, and R. Hum. Accelerated ATPG and fault grading via testability analysis. In Proceedings of IEEE Int. Symposium on Circuits and Systems, pages 695–698, 1985. The ISCAS85 benchmark netlist are available via ftp://www.mcnc.mcnc.org..
  9. 9.
    W.E. Hart and R.K. Belew. Optimization with genetic algorithms hybrids that use local search. In R.K. Belew and M. Mitchell, editors, Adaptive Individuals in Evolving Populations. Addison Wesley, 1996.Google Scholar
  10. 10.
    D.H. Ackely and M.L. Littman. A case for lamarckian evolution. In C.G. Langton, editor, Artificial Life III. Addison Wesley, 1994.Google Scholar
  11. 11.
    Y. Li, K.C. Tan, and M. Gong. Model reduction in control systems by means of global structure evolution and local parameter learning. In D. Dasgupta and Z. Michalewicz, editors, Evolutionary Algorithms in Engineering Applications. Springer Verlag, 1996.Google Scholar
  12. 12.
    J.J. Grefenstette. Lamarckian learning in multi-agent environments. In Proc. 4th Intl. Conference on Genetic Algorithms. Morgan Kauffman, 1991.Google Scholar
  13. 13.
    M. Potter and K. de Jong. A cooperative coevolutionary approach to function optimization, 1994.Google Scholar
  14. 14.
    Mitchell A. Potter, Kenneth A. De Jong, and John J. Grefenstette. A coevolutionary approach to learning sequential decision rules. In Larry Eshelman, editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 366–372, San Francisco, CA, 1995. Morgan Kaufmann.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Evelina Lamma
    • 1
  • Luís MonizPereira
    • 2
  • Fabrizio Riguzzi
    • 1
  1. 1.Dipartimento di IngegneriaUniversità di FerraraFerraraItaly
  2. 2.Centro de Inteligência Artificial (CENTRIA), Departamento de Informática, Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

Personalised recommendations