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

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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.

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Notes

  1. When we speak about equilibrium in complex systems we must distinguish two kinds of equilibrium: the static equilibrium and the dynamic equilibrium. Usually this division is made for a better understanding of the mechanisms which act in living and inert entities. The main difference is that the static equilibrium can be achieved without energy consumption, while a dynamic equilibrium requires energy consumption according to the principles of thermodynamics. For this reason is it usually stated that a biological entity is in a steady-state instead of equilibrium.

  2. According to Eq. 1 the partial derivative of the stimulus J is zero, meaning that the control variable x will stay permanently in the same value.

  3. We mean by thought as the continuous brain’s neural activity that supports any kind of behavior, such as decision making, memory, perceptual, attentional and homeostatic processes.

References

  • 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):134117

    Google Scholar 

  • Aubin J-P (1991) Viability theory. Birkhäuser, Cambridge

    MATH  Google Scholar 

  • Borenstein J, Koren Y (1989) Real-time obstacle avoidance for fast mobile robots. IEEE Trans Syst Man Cybern 19(5):1179–1187

    Article  Google Scholar 

  • Borenstein J, Koren Y (1991) The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans Rob Autom 7(3):278–288

    Article  Google Scholar 

  • Butz M, Sigaud O, Gérard P (eds) (2003) Anticipatory behavior in adaptive learning systems, foundations, theories, and systems, vol 2684 of LNCS. Springer

  • Cannon W (1932) The wisdom of the body. W.W. Norton & Company, Inc., New York

    Google Scholar 

  • 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 506–515

  • Davidsson P (1997) Linearly anticipatory autonomous agents. In Agents, pp 490–491. http://doi.acm.org/10.1145/267658.267784

  • de Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence paradigm. Springer Verlag, London

    Google Scholar 

  • Driver P, Humphries D (1988) Protean behavior: the biology of unpredictability. Oxford University Press, Oxford

    Google Scholar 

  • Edelman GM (1987) Neural Darwinism—the theory of neuronal group selection. Basic Books, New York

    Google Scholar 

  • Edelman GM, Tononi G (2002) El Universo de la Conciencia, 1st edn. Crítica, Spain

  • Holland JH (1971a) Processing and processors for schemata. In: Jacks EL (ed) Associative information techniques. American Elsevier, New York, pp 127–146

    Google Scholar 

  • 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 43–46

  • Holland JH (1975) Adaptation in natural artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Holland JH, Reitman JS (1977) Cognitive systems based on adaptive algorithms. SIGART Bull 1(63):49

    Article  Google Scholar 

  • 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 450:503–508

    Article  Google Scholar 

  • Latombe J-C (1991) Robot motion planning. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Maes P (1989) The dynamics of action selection. In: Proceedings of the eleventh international joint conference on artificial intelligence (IJCAI-89). Detroit, MI, pp 991–997

  • 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 Science

  • 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 411–420

    Google Scholar 

  • Rimon E (1990) Exact robot navigation using artificial potential functions. PhD. Thesis, Yale University

  • Rosen R (1985) Anticipatory systems. Pergamon Press, Oxford

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    MATH  MathSciNet  Google Scholar 

  • Shannon CE, Weaver W (1949) The mathematical theory of communication. University of Illinois Press, Illinois

    MATH  Google Scholar 

  • Thorndike EL (1927) The law of effect. Am J Psychol 39:212–222

    Article  Google Scholar 

  • Tononi G, Edelman GM (1998) Consciousness and complexity. Science 282(5395):1846–1851

    Article  Google Scholar 

  • Warren C (1989) Global path planning using artificial potential fields. In: IEEE international conference on robotics and automation, vol 1. IEEE, New York, pp 316–321

  • Wiener N (1963) Kybernetik. Econ-Verlag, Düsseldorf

    Google Scholar 

  • Wilson S (1991) The animat path to AI. In: Meyer J-A, Wilson SW (eds) From animals to animats. The MIT Press, Cambridge, pp 15–21

    Google Scholar 

Download references

Acknowledgment

This work has been partially funded by the Spanish Ministry of Science and Technology; project DPI2006-15346-C03-02.

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Correspondence to José Antonio Martín H..

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Martín H., J.A., de Lope, J. & Maravall, D. Adaptation, anticipation and rationality in natural and artificial systems: computational paradigms mimicking nature. Nat Comput 8, 757–775 (2009). https://doi.org/10.1007/s11047-008-9096-6

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