Natural Computing

, 8:757

Adaptation, anticipation and rationality in natural and artificial systems: computational paradigms mimicking nature

  • José Antonio Martín H.
  • Javier de Lope
  • Darío Maravall
Article

Abstract

Intelligence, Rationality, Learning, Anticipation and Adaptation are terms that have been and still remain in the central stage of computer science. These terms delimit their specific areas of study; nevertheless, they are so interrelated that studying them separately is an endeavor that seems little promising. In this paper, a model of study about the phenomena of Adaptation, Anticipation and Rationality as nature-inspired computational paradigms mimicking nature is proposed by means of a division, which is oriented, towards the discrimination of these terms, from the point of view of the complexity exhibited in the behavior of the systems, where these phenomena come at play. For this purpose a series of fundamental principles and hypothesis are proposed as well as some experimental results that corroborate them.

Keywords

Adaptation Anticipation Rationality Nature-inspired paradigms 

References

  1. Abraham NL, Probert MIJ (2008) Improved real-space genetic algorithm for crystal structure and polymorph prediction. Phys Rev B Condens Matter Mater Phys 77(13):134117Google Scholar
  2. Aubin J-P (1991) Viability theory. Birkhäuser, CambridgeMATHGoogle Scholar
  3. Borenstein J, Koren Y (1989) Real-time obstacle avoidance for fast mobile robots. IEEE Trans Syst Man Cybern 19(5):1179–1187CrossRefGoogle Scholar
  4. Borenstein J, Koren Y (1991) The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans Rob Autom 7(3):278–288CrossRefGoogle Scholar
  5. Butz M, Sigaud O, Gérard P (eds) (2003) Anticipatory behavior in adaptive learning systems, foundations, theories, and systems, vol 2684 of LNCS. SpringerGoogle Scholar
  6. Cannon W (1932) The wisdom of the body. W.W. Norton & Company, Inc., New YorkGoogle Scholar
  7. Cliff D, Miller GF (1996) Co-evolution of pursuit and evasion II: simulation methods and results. In: Maes P, Mataric MJ, Meyer J-A, Pollack JB, Wilson SW (eds) From animals to animats 4. Proceedings of the fourth international conference on simulation of adaptive behaviour. MIT Press, Cambridge, MA, pp 506–515Google Scholar
  8. Davidsson P (1997) Linearly anticipatory autonomous agents. In Agents, pp 490–491. http://doi.acm.org/10.1145/267658.267784
  9. de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence paradigm. Springer Verlag, LondonGoogle Scholar
  10. Driver P, Humphries D (1988) Protean behavior: the biology of unpredictability. Oxford University Press, OxfordGoogle Scholar
  11. Edelman GM (1987) Neural Darwinism—the theory of neuronal group selection. Basic Books, New YorkGoogle Scholar
  12. Edelman GM, Tononi G (2002) El Universo de la Conciencia, 1st edn. Crítica, SpainGoogle Scholar
  13. Holland JH (1971a) Processing and processors for schemata. In: Jacks EL (ed) Associative information techniques. American Elsevier, New York, pp 127–146Google Scholar
  14. Holland JH (1971b) Schemata and intrinsically parallel adaptation. In: Proceedings of the NSF workshop of learning system theory and its applications. University of Florida, Gainesville, pp 43–46Google Scholar
  15. Holland JH (1975) Adaptation in natural artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  16. Holland JH, Reitman JS (1977) Cognitive systems based on adaptive algorithms. SIGART Bull 1(63):49CrossRefGoogle Scholar
  17. Kobayakawa K, Kobayakawa R, Matsumoto H, Oka Y, Imai T, Ikawa M, Okabe M, Ikeda T, Itohara S, Kikusui T, Mori K, Sakano H (2007) Innate versus learned odor processing in the mouse olfactory bulb. Nature 450:503–508CrossRefGoogle Scholar
  18. Latombe J-C (1991) Robot motion planning. Kluwer Academic Publishers, BostonGoogle Scholar
  19. Maes P (1989) The dynamics of action selection. In: Proceedings of the eleventh international joint conference on artificial intelligence (IJCAI-89). Detroit, MI, pp 991–997Google Scholar
  20. Mathias KE, Schaffer JD, Eshelman LJ, Mani M (1998) The effects of control parameters and restarts on search stagnation in evolutionary programming. In: Eiben AE, Bäck T, Schoenauer M, Schwefel HP (eds) Parallel problem solving from nature—PPSN V vol 1498 of LNCS. Springer, Berlin, pp 398–407. Lecture Notes in Computer ScienceGoogle Scholar
  21. Miller GF, Cliff D (1994) Protean behavior in dynamic games: arguments for the co-evolution of pursuit-evasion tactics. In: Cliff D, Husbands P, Meyer J-A, Wilson SW (eds) From animals to animats 3: proceedings of the third international conference on simulation of adaptive behavior. The MIT Press, Cambridge, MA, pp 411–420Google Scholar
  22. Rimon E (1990) Exact robot navigation using artificial potential functions. PhD. Thesis, Yale UniversityGoogle Scholar
  23. Rosen R (1985) Anticipatory systems. Pergamon Press, OxfordGoogle Scholar
  24. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423MATHMathSciNetGoogle Scholar
  25. Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, IllinoisMATHGoogle Scholar
  26. Thorndike EL (1927) The law of effect. Am J Psychol 39:212–222CrossRefGoogle Scholar
  27. Tononi G, Edelman GM (1998) Consciousness and complexity. Science 282(5395):1846–1851CrossRefGoogle Scholar
  28. Warren C (1989) Global path planning using artificial potential fields. In: IEEE international conference on robotics and automation, vol 1. IEEE, New York, pp 316–321Google Scholar
  29. Wiener N (1963) Kybernetik. Econ-Verlag, DüsseldorfGoogle Scholar
  30. Wilson S (1991) The animat path to AI. In: Meyer J-A, Wilson SW (eds) From animals to animats. The MIT Press, Cambridge, pp 15–21Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • José Antonio Martín H.
    • 1
  • Javier de Lope
    • 2
  • Darío Maravall
    • 3
  1. 1.Department of Sistemas Informáticos y ComputaciónUniversidad Complutense de MadridMadridSpain
  2. 2.Department of Applied Intelligent SystemsUniversidad Politécnica de MadridMadridSpain
  3. 3.Department of Artificial IntelligenceUniversidad Politécnica de MadridMadridSpain

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