skip to main content
10.1145/2001576.2001610acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Digital enzymes: agents of reaction inside robotic controllers for the foraging problem

Published:12 July 2011Publication History

ABSTRACT

Over billions of years, natural selection has continued to select for a framework based on (1) parallelism and (2) cooperation across various levels of organization within organisms to drive their behaviors and responses. We present a design for a bottom-up, reactive controller where the agent's response emerges from many parallelized, enzymatic interactions (bottom-up) within the biologically-inspired process of signal transduction (reactive). We use enzymes to explore the potential for evolving simulated robot controllers for the central-place foraging problem. The properties of the robot and stimuli present in its environment are encoded in a digital format ("molecule") capable of being manipulated and altered through self-contained computational programs ("enzymes") executing in parallel inside each controller to produce the robot's foraging behavior. Evaluation of this design in unbounded worlds reveals evolved strategies employing one or more of the following complex behaviors: (1) swarming, (2) coordinated movement, (3) communication of concepts using a primitive language based on sound and color, (4) cooperation, and (5) division of labor.

References

  1. B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, and P. Walter. Molecular Biology of the Cell, Fourth Edition. Garland Science, 4 edition, 2002.Google ScholarGoogle Scholar
  2. F. H. Bennett III. Automatic creation of an efficient multi-agent architecture using genetic programming with architecture-altering operations. In Proceedings of the First Annual Conference on Genetic Programming, pages 30--38. MIT Press, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. F. Bernardini, M. Gheorghe, and N. Krasnogor. Quorum sensing P systems. Theoretical Computer Science, 371:20--33, February 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Campo and M. Dorigo. Efficient multi-foraging in swarm robotics. In Proceedings of the 9th European Conference on Advances in Artificial Life, ECAL'07, pages 696--705, Lisbon, Portugal, 2007. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. D. Christian. Maps of Time: An Introduction to Big History (California World History Library, 2). University of California Press, February 2004.Google ScholarGoogle Scholar
  6. C. Di Chio and P. Di Chio. Group-foraging with particle swarms and genetic programming. In Proceedings of the 10th European Conference on Genetic Programming, EuroGP'07, pages 331--340, Valencia, Spain, 2007. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. J. Falke, R. B. Bass, S. L. Butler, S. A. Chervitz, and M. A. Danielson. The two-component signaling pathway of bacterial chemotaxis: a molecular view of signal transduction by receptors, kinases, and adaptation enzymes. Annual Review of Cell and Developmental Biology, 13:457--512, 1997.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Gheorghe, C. Martín-Vide, V. Mitrana, and M. J. Pérez Jiménez. An agent based approach of collective foraging. In Proceedings of the Artificial and Natural Neural Networks 7th International Conference on Computational Methods in Neural Modeling - Volume 1, IWANN'03, pages 638--645, Maó, Menorca, Spain, 2003. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. R. Koza, J. Roughgarden, and J. P. Rice. Evolution of food-foraging strategies for the caribbean anolis lizard using genetic programming. Adaptive Behavior, 1:171--199, October 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. Leja. The National Human Genome Research Institute, 2009. http://www.accessexcellence.org/RC/VL/GG/enzyme.php.Google ScholarGoogle Scholar
  11. W. Liu and A. F. T. Winfield. Modeling and optimization of adaptive foraging in swarm robotic systems. International Journal of Robotics Research, 29:1743--1760, December 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. C. Ofria and C. O. Wilke. Avida: a software platform for research in computational evolutionary biology. Artificial Life, 10(2):191--229, March 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. S. Parkinson. Signal transduction schemes of bacteria. Cell, 73(5):857--871, June 1993.Google ScholarGoogle ScholarCross RefCross Ref
  14. E. Pennisi. Jumping genes hop into the evolutionary limelight. Science, 317(5840):894--895, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  15. G. Puaun and F. J. Romero-Campero. Membrane computing as a modeling framework: cellular systems case studies. In Proceedings of the Formal Methods for the Design of Computer, Communication, and Software Systems, SFM'08, pages 168--214, Bertinoro, Italy, 2008. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. P. G. Saffman and M. Delbrück. Brownian motion in biological membranes. Proceedings of the National Academy of Sciences of the United States of America, 72(8):3111--3113, Aug. 1975.Google ScholarGoogle ScholarCross RefCross Ref
  17. M. Sipper. The evolution of parallel cellular machines: toward evolware. Biosystems, 42(1):29--43, March 1997.Google ScholarGoogle ScholarCross RefCross Ref
  18. I. Steffan-Dewenter and A. Kuhn. Honeybee foraging in differentially structured landscapes. Proceedings of the Royal Society of London - Biological Sciences, 270:569--575, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  19. D. J. T. Sumpter and M. Beekman. From nonlinearity to optimality: pheromone trail foraging by ants. Animal Behavior, 66:273--280, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  20. J. A. Walker, K. Völk, S. L. Smith, and J. F. Miller. Parallel evolution using multi-chromosome cartesian genetic programming. Genetic Programming and Evolvable Machines, 10:417--445, December 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Digital enzymes: agents of reaction inside robotic controllers for the foraging problem

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader