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Modeling Lamarckian Evolution: From Structured Genome to a Brain-Like System

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ICT Innovations 2012 (ICT Innovations 2012)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 207))

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Abstract

The paper addresses development of a brain-like system based on Lamarckian view toward evolution. It describes a development of an artificial brain, from an artificial genome, through a neural stem cell. In the presented design a modulon level of genetic hierarchical control is used. In order to evolve such a system, two environments are considered, genetic and behavioral. The genome comes from the genetic environment, evolves into an artificial brain, and then updates the memory through interaction with the behavioral environment. The updated genome can then be sent back to the genetic environment. The memory units of the artificial brain are synaptic weights which in this paper represent achievement motivations of the agent, so they are updated in relation to particular achievement. A simulation of the process of learning by updating achievement motivations in the behavioral environment is also shown.

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References

  1. Cangelosi, A., Parisi, D., Nolfi, S.: Cell division and migration in a ‘genotype’ for neural networks. Network 5, 497–515 (1994)

    Article  MATH  Google Scholar 

  2. Vaario, J., Ogata, N., Shimohara, K.: Synthesis of environment directed and genetic growth. In: Artificial Life V, pp. 207–214. Foundation of Advancement of International Science, Nara (1996)

    Google Scholar 

  3. Eggenberger, P.: Creation of neural networks based on developmental and evolutionary principles. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, Springer, Heidelberg (1997)

    Google Scholar 

  4. Bull, L.: On the evolution of multicellularity and eusociality. Journal of Artificial Life 5(1), 1–15 (1999)

    Article  Google Scholar 

  5. Reil, T.: Dynamics of gene expression in an artificial gene – Implications for biological and artificial ontogeny. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 457–466. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  6. Nehaniv, C.L., Hewitt, J., Christiansen, B., Wernick, P.: What Software Evolution and Biological Evolution Don’t Have in Common. In: Second International IEEE Workshop on Software Evolvability, SE 2006, pp. 58–65 (2006)

    Google Scholar 

  7. Perlovsky, L.I.: Integrated Emotions, Cognition, and Language. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 1570–1575 (2006)

    Google Scholar 

  8. Baluja, S.: Evolution of an artificial neural network based autonomousland vehicle controller. IEEE Trans. Syst. Man Cybern. 26(3), 450–463 (1996)

    Article  Google Scholar 

  9. Floreano, D., Kato, T., Marocco, D., Sauser, E.: Coevolution of activevision and feature selection. Biol. Cybern. 90(3), 218–228 (2004)

    Article  MATH  Google Scholar 

  10. Kohl, N., Stanley, K., Miikkulainen, R., Samples, M., Sherony, R.: Evolving a real-world vehiclewarning system. In: Proc. Genetic Evol. Comput. Conf., pp. 1681–1688 (2006)

    Google Scholar 

  11. McLelland, D.: The Achieving Society. Van Nostrand, Princeton (1961)

    Google Scholar 

  12. Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)

    MATH  Google Scholar 

  13. Pfeifer, R., Scheier, C.: Understanding Intelligence. The MIT Press (2000)

    Google Scholar 

  14. Jones, T.: Artificial Intelligence – A Systems Approach. Infinity Science Press (2008)

    Google Scholar 

  15. Elman, J.: Learning and development in neural networks: The importance of starting small. Cognition 48, 71–99 (1993)

    Article  Google Scholar 

  16. Gruau, F., Whitley, D.: Adding learning to the cellular development of neural networks. Evolutionary Computation (1-3), 213–233 (1993)

    Google Scholar 

  17. Nolfi, S., Miglino, O., Parisi, D.: Phenotypic plasticity in evolving neural networks. In: Graussier, D., Nicoud, J.-D. (eds.) International Conference from Perception to Action, pp. 146–157. IEEE Computer Society Press, Los Alamitos (1994)

    Google Scholar 

  18. Bozinovski, S.: A self-learning system using secondary reinforcement. In: Trappl, R. (ed.) Cybernetics and Systems Research, pp. 397–402. North-Holland (1982)

    Google Scholar 

  19. Barinaga, M.: Newborn neurons search for meaning. Science (299), 32–34 (2003)

    Google Scholar 

  20. Buell, D., El-Ghazawi, T., Gaj, K., Kindratenko, V.: High performance reconfigurable computing. IEEE Computer, 23–27 (2007)

    Google Scholar 

  21. Cooper, L., Nathan, I., Blais, B., Shouval, H.: Theory of Cortical Plasticity. World Scientific (2004)

    Google Scholar 

  22. Bozinovski, S.: Crossbar Adaptive Array: The First Connectionist Network that Solved the Delayed Reinforcement Learning Problem. In: Dobnikar, A., Steele, N., Pearson, D., Alberts, R. (eds.) Artificial Neural Networks and Genetic Algorithms, pp. 320–325. Springer (1999)

    Google Scholar 

  23. Bozinovski, S., Bozinovska, L.: Self-learning agents: A connectionist theory of emotion, based on crossbar value judgment. Cybernetics and Systems: An International Journal 32, 637–669 (2001)

    MATH  Google Scholar 

  24. Bozinovski, S., Bozinovska, L.: Evolution of a Cognitive Architecture for Emotional Learning from a Modulon-structured Genome. Journal of Mind and Behavior 29(1-2), 195–216 (2008)

    Google Scholar 

  25. Aube’, M., Senteni, A.: What are emotions for? Commitments management and regulation within animals/animats encounters. In: Maes, P., Mataric, M., Mayer, J.-A., Pollack, J., Wilson, S. (eds.) From Animals to Animats 4, pp. 246–271. MIT Press (1996)

    Google Scholar 

  26. Botelho, L., Coelho, H.: Emotion-based attention shift in autonomous agents. In: Müller, J.P., Wooldridge, M.J., Jennings, N.R. (eds.) ECAI-WS 1996 and ATAL 1996. LNCS, vol. 1193, pp. 277–291. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  27. Canamero, D., Numaoka, C., Petta, P. (eds.) Grounding Emotions in Adaptive Systems. Workshop Proceedings, Simulation of Adaptive Behavior Conference, Zurich (1998)

    Google Scholar 

  28. Petta, P., Trappl, R.: Personalities for synthetic actora: Current issues and some perspectives In R. In: Trappl, R., Petta, P. (eds.) Creating Personalities for Synthetic Actors. LNCS (LNAI), vol. 1195, pp. 209–218. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  29. Gadanho, S., Hallam, J.: Robot learning driven by emotions. Adaptive Behavior 9(1), 42–64 (2002)

    Article  Google Scholar 

  30. Bozinovski, S., Schoel, P.: Emotions and hormones in learning: A biology inspired control architecture for a trainable goalkeeper robot. GMD Report 73. German National Center for Information Technology, Sankt Augustin, Germany (1999)

    Google Scholar 

  31. Castelfranchi, C.: Affective appraisal vs. cognitive evaluation in social emotions and interactions. In: Paiva, A.M. (ed.) Affective Interactions. LNCS(LNAI), vol. 1814, pp. 76–106. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  32. Brave, S., Nass, C.: Emotion in human-computer interaction. In: Jacko, J., Sears, A. (eds.) The Human-Computer Interaction Handbook, pp. 81–96. Lawrence Erlbaum Associates (2003)

    Google Scholar 

  33. Yu, C., Xu, L.: An emotion-based approach to decision making and self learning in autonomous robot control. In: Proc. IEEE 8th World Congress on Intelligent Control and Automation, Hangzou, China, pp. 2386–2390 (2004)

    Google Scholar 

  34. Fellous, J.-M., Arbib, M.: Who Needs Emotions? The Brain Meets the Robot. Oxford University Press (2005)

    Google Scholar 

  35. Minsky, M.: The Emotion Machine. Simon and Schuster (2006)

    Google Scholar 

  36. Darwin, C.: On the Origin of Species by Means of Natural Selection. John Murray, London (1859)

    Google Scholar 

  37. Lamarck, J.: Philosophie Zoologique. Oxford Univ. Press, Oxford (1809)

    Google Scholar 

  38. Parker, M., Bryant, B.: Lamarckian neuroevolution for visual control in the Quake II environment. In: Proc. IEEE Congr. Evol. Comput., pp. 2630–2637 (2009)

    Google Scholar 

  39. Gançarski, P., Blansché, A.: Darwinian, Lamarckian, and Baldwinian (Co)Evolutionary Approaches for Feature Weighting in K-MEANS-Based Algorithms. IEEE Transactions on Evolutionary Computation 12(5), 617–629 (2008)

    Article  Google Scholar 

  40. Bozinovski, S., Bozinovska, L.: Flexible production lines in genetics: A model of protein biosynthesis process. In: Proc. Int. Conf. on Robotics, pp. 1–4 (1987)

    Google Scholar 

  41. Demeester, L., Eichler, K., Loch, C.: Organic production systems: What the biological cell can teach us about manufacturing. Manufacturing and Service Operations management 6(2), 115–132 (2004)

    Article  Google Scholar 

  42. Bozinovski, S., Jovancevski, G., Bozinovska, N.: DNA as a real time, database operating system. In: Proc. SCI 2001, Orlando, pp. 65–70 (2001)

    Google Scholar 

  43. Danchin, A., Noria, S.: Genome structures, operating systems and the image of a machine. In: Vincente, M., Tamames, J., Valencia, A., Mingorance, J. (eds.) Molecules in Time and Space. Kluwer (2004)

    Google Scholar 

  44. Lengeler, J., Mueller, B., di Primio, F.: Cognitive abilities of unicellular mechanisms. GMD Report 57, Institute for Autonomous Intelligent Systems, German National Research Center for Information Technologies, Sankt Augustin (1999) (in German)

    Google Scholar 

  45. Sakaguchi, D., Van Hofelen, S., Grozdanic, S., Kwon, Y., Kardon, R., Young, M.: Neural progenitor cell transplants into the developing and mature central nervous system. In: Ourednik, J., Ourednik, V., Sakaguchi, D., Nilsen-Hamilton, M. (eds.) Stem Cell Biology. Annals of the New Your Academy of Sciences, vol. (1049), pp. 118–134 (2005)

    Google Scholar 

  46. McCulloch, W., Pitts, W.: A logical calculus of the ideas immanent from nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Biological Sciences and Physical Sciences, South Carolina State University, USA, Orangeburg

    Liljana Bozinovska

  2. Faculty of Computer Science and Engineering, University Sts Cyril and Methodius, Macedonia, Skopje

    Nevena Ackovska

Authors
  1. Liljana Bozinovska
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  2. Nevena Ackovska
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Correspondence to Liljana Bozinovska .

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Editors and Affiliations

  1. , Faculty of Computer Science, Ss Cyrill and Methodius University, Ruger Boskovic 16, POBox 393, Skopje, 1000, Macedonia

    Smile Markovski

  2. , Faculty of Information Sciences, Ss Cyrill and Methodius University, Ruger Boskovic 16, Skopje, 1000, Macedonia

    Marjan Gusev

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Bozinovska, L., Ackovska, N. (2013). Modeling Lamarckian Evolution: From Structured Genome to a Brain-Like System. In: Markovski, S., Gusev, M. (eds) ICT Innovations 2012. ICT Innovations 2012. Advances in Intelligent Systems and Computing, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37169-1_9

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  • DOI: https://doi.org/10.1007/978-3-642-37169-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37168-4

  • Online ISBN: 978-3-642-37169-1

  • eBook Packages: EngineeringEngineering (R0)

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