Interactive and emergent computing in ant colonies
DOI:
https://doi.org/10.48168/cc012020-001Keywords:
Complex systems , interactive computing, emerging computing, ant colonies, foraging, navigation system in antsAbstract
Living systems process information; this is what they do for a living. The ability to process information is, in fact, one of the most salient features of complex adaptive systems in general. It is generally accepted, from biology and computer science, that the standard model of algorithmic computing, represented by the Turing machine, establishes the theoretical limits of what life can and cannot compute. Despite this, the type of computations performed by living systems differs in fundamental ways from those supported by the Turing machine. This article demonstrates that the navigation system employed individually by the ants and the collective strategies for optimal food collection used by the colony cannot be reduced to forms of algorithmic computing. This idea justifies the need to develop new computational models that allow us to unravel computational logic and the complexity of life.
References
Almér, A., Dodig-Crnkovic, G. & von Haugwitz, R. (2015). Collective cognition and distributed information processing from bacteria to humans. In: AIBS Convention 2015 (code 112552). Canterbury: University of Kent.
Amos, M., Hodgson, D. A. & Gibbons, A. (2007). Bacterial self-organisation and computation. International Journal of Unconventional Computing, 3(3), 199-210.
Andel, D. & Wehner, R. (2004). Path integration in desert ants, Cataglyphis: how to make a homing ant run away from home. Proceedings of the Royal Society B, 271(1547), 1485-1489.
Barrett, L. (2011). Beyond the Brain: How Body and Environment Shape Animal and Human Minds. Princeton: Princeton University Press.
Bonabeau, E. (1998). Social insect colonies as complex adaptive systems. Ecosystems, 1(5), 437-443.
Bonabeau, E., Dorigo, M. & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. New York: Oxford University Press.
Bonabeau, E., Theraulaz, G., Deneubourg, J. L., Aron, S. & Cama¬zine, S. (1997). Self-organization in social insects. Trends in Ecology & Evolution, 12(5), 188-193. Brenner, S. (2012). Life’s code script. Nature, 482(7386), 461.
Camazine, S., Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G. & Bonabeau, E. (2001). Self-Organization in Biological Systems. Princeton: Princeton University Press.
Cleland, C. E. (2006). The Church–Turing thesis. A last vestige of a failed mathematical program. In A. Olszewski, J. Woleński & R. Janusz (Eds.), Church’s Thesis After 70 Years (pp. 119-146). Frankfurt: Ontos.
Collete, T. (2019). Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms. Journal of Experimental Biology, 222(11), jeb205187.
Da Costa, N. & Doria, F. A. (2013). Metamathematical limits to computation. In K. Nakamatsu & L. C. Jain (Eds.), The Handbook on Reasoning-Based Intelligent Systems (pp. 119-141). New Jersey: World Scientific.
Dodig-Crnkovic, G. (2011). Dynamics of information as natural computation. Information, 2(3), 460-477.
Dodig-Crnkovic, G. (2011a). Significance of models of computation, from Turing model to natural computation. Minds and Machines, 21(2), 301-322.
Dodig-Crnkovic, G. (2010). Biological information and natural computation. In J. Vallverdú (Ed.), Thinking Machines and the Philosophy of Computer Science: Concepts and Principles (pp. 36-52). Hershey, PA: IGI Global.
Dornhaus, A. & Franks, N. R. (2008). Individual and collective cognition in ants and other insects (Hymenoptera: Formicidae). Myrmecological News, 11, 215-226.
Dorigo, M. & Stützle, T. (2004). Ant Colony Optimization. Cambridge, MA: MIT Press.
Eberbach, E., Goldin, D. & Wegner, P. (2004). Turing’s ideas and models of computation. In: C. Teuscher (Ed.), Alan Turing: Life and Legacy of a Great Thinker (pp. 159-194). Berlin: Springer.
Feinerman, O. & Korman, A. (2017). Individual versus collective cognition in social insects. Journal of Experimental Biology, 220(1), 73-82.
Forbes, N. (2004). Imitation of Life: How Biology Is Inspiring Computing. Cambridge, MA: MIT Press.
Franks, N. R. (1989). Army ants: A collective intelligence. American Scientist, 77(2), 138-145.
Goldin, D., Smolka, S. A., Attie, P. & Sonderegger, E. (2004). Turing machines, transition systems, and interaction. Information & Computation Journal, 194(2), 101-128.
Goldin, D. & Wegner, P. (2008). The interactive nature of computing: Refuting the strong Church-Turing thesis. Minds & Machines, 18(1), 17-38.
Gómez-Cruz, N. A. (2018). Simulación basada en agentes: una metodología para el estudio de sistemas complejos. In M. L. Eschenhagen, G. Velez, Guerrero, G. & C. E. Maldonado (Eds.), Construcción de problemas de investigación: diálogos entre el interior y el exterior (páginas 230-268). Medellín: Universidad de Antioquia.
Gómez-Cruz, N. A. (2013). Vida Artificial: Ciencia e Ingeniería de Sistemas Complejos. Bogotá: Universidad del Rosario.
Gómez-Cruz, N. A. & Maldonado, C. E. (2011). Biological compu¬tation: A road to complex engineered systems. In H. Sayama, A. Minai, D. Braha & Y. Bar-Yam (Eds.), Unifying Themes in Complex Systems Volume VIII: Proceedings of the Eighth International Conference on Complex Systems (pp 918-927). Cambridge, MA: NECSI Knowledge Press.
Gómez-Cruz, N. A. & Niño, L. F. (2020). Computación biológica: el estudio de la naturaleza computacional de los sistemas vivos. En C. E. Maldonado (Ed.). Biología Teórica, Explica¬ciones y Complejidad (capítulo 7). Bogotá, Universidad del Bosque.
Gordon, D. (2016a). Collective wisdom of ants. Scientific American, 314(2), 44-47.
Gordon, D. (2016b). The evolution of the algorithms for collective behavior. Cell Systems, 3(6), 514-520.
Gordon, D. (2010). Ant Encounters: Interaction Networks and Colony Behavior. Princeton: Princeton University Press.
Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F. et al. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), 987-991.
Hartmann, G. & Wehner, R. (1995). The ant’s path integration system: a neural architecture. Biological Cybernetics, 73(6), 483-497.
Heinze, S., Narendra, A. & Cheung, A. (2018). Principles of insect path integration. Current Biology, 28(17), R1043-R1058.
Hewitt, C. (2013). What is computation? Actor model vs. Turing’s model. In H. Zenil (Ed.), A Computable Universe. Understanding Computation and Exploring Nature as Computation (pp. 159-186). Singapore: World Scientific.
Hölldobler, B. & Wilson, E. O. (2014). El Superorganismo. Belle¬za y Elegancia de las Asombrosas Sociedades de Insectos. Buenos Aires: Katz Editores.
Hölldobler, B. & Wilson, E. O. (1990). The Ants. Berlin: Springer.
Horváth, G. & Varjú, D. (2004). Polarized Light in Animal Vision: Polarization Patterns in Nature. Berlin: Springer.
Kari, L. & Rozenberg, G. (2008). The many facets of natural com¬puting. Communications of the ACM, 51(10), 72-83.
MacLennan, B. J. (2004). Natural computation and non-Turing models of computation. Theoretical Computer Science, 317(1-3), 115-145.
Maldonado, C. E. & Gómez-Cruz, N. A. (2015). Biological hypercomputation: A new research problem in complexity theory. Complexity, 20(4), 8-18.
Mitchell, M. (2009). Complexity: A Guided Tour. Oxford: Oxford University Press.
Mitchell. M. (2012). Biological computation. The Computer Journal, 55(7), 852-855.
Moussaid, M., Garnier, S., Theraulaz, G. & Helbing, D. (2009). Collective information processing and pattern formation in swarms, flocks y crowds. Topics in Cognitive Science, 1(3), 469-497.
Müller, M. & Wehner, R. (1988). Path integration in desert ants, Cataglyphis fortis. Proceedings of the National Academy of Sciences, 85(14), 5287-5290.
National Geographic (2011). Hermandad de tejedoras. Retrieved from http://www.nationalgeographic.com.es/mundo-ng/grandes-reportajes/hermandad-de-tejedoras_4190/1
Newell, A. & Simon, H. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113-126.
Ronacher, B. (2008). Path integration as the basic navigation mechanism of the desert ant Cataglyphis fortis. Myrmecological News, 11, 53-62.
Shettleworth, S. J. (2001). Animal cognition and animal behavior. Animal Behavior, 61(2), 277-286.
Siegelmann, H. T. (2013). Turing on super-Turing and adaptivity. Progress in Biophysics & Molecular Biology, 113(1), 117-126.
Solé, R., Moses, M. & Forrest, S. (2019). Liquid brains, solid brains. Philosophical Transactions B, 374, 20190040.
Solé, R. & Macia, J. (2011). Synthetic biocomputation: the possible and the actual. In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo & R. Doursat (Eds), Advances in Ar¬tificial Life, ECAL, 2011 (without numeration). Cambridge, MA: MIT Press.
Stieb, S. (2010). Frontal overview of the brain of the desert ant Cataglyphis fortis. Rettrieved from http://www.graduateschools.uni-wuerzburg.de/life_sciences/gsls_newsletter/
Syropoulos, A. (2008). Hypercomputation: Computing Beyond the Church-Turing Barrier. New York: Springer.
Thiélin-Bescond, M. & Beugnon, G. (2005). Vision-independent odometry in the ant Cataglypgis cursor. Naturwissenschaften, 92(4), 193-197.
Wegner, P. (1997). Why interaction is more powerful than algori¬thms. Communication of the ACM, 40 (5), 80-91.
Wegner, P. (1998). Interactive foundations of computing. Theore¬tical Computer Science, 192 (2), 315-351.
Wehner, R. (2003). Desert ant navigation: how miniature brain solve complex systems. Journal of Comparative Physiology A, 189(8), 579-588.
Wehner, R., Boyer, M., Loertscher, F., Sommer, S. & Menzi, U. (2006). Ant navigation: one-way routes rather than maps. Current Biology, 16(1), 75-79.
Wehner, R. & Srinivasan, M. V. (2003). Path integration in insects. In K. J. Jeffery (Ed.), The Neurobiology of Spatial Behaviour (pp. 9-30). Oxford: Oxford University Press.
Whener, R. & Whener, R. (1990). Insect navigation: use of maps or Ariadne’s thread. Ecology & Evolution, 2(1), 27-48.
Wilensky, U. (1997). NetLogo Ants model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved from http://ccl.nor¬thwestern.edu/netlogo/models/Ants.
Wilensky, U. (1999). NetLogo. Evanston, IL: Center for Connec¬ted Learning and Computer-Based Modeling, Northwestern University. Downloaded from http://ccl.northwestern.edu/netlogo/.
Wilensky, U. & Rand, W. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems whit NetLogo. Cambridge, MA: MIT Press.
Wittlinger, M., Whener, R. & Wolf, H. (2006). The ant odometer: stepping on stilts and stumps. Science, 312(5782), 1965-1967.
Wittlinger, M., Wolf, H. & Whener, R. (2007). Hair plate mechano¬receptors associated with body segments are not necessary for three-dimensional path integration in desert ants, Cataglyphis fortis. Journal of Experimental Biology, 210(3), 375-382.
Wolf, H. (2011). Odometry and insect navigation. Journal of Expe¬rimental Biology, 214(10): 1629-1641.
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