Abstract
Spatial multi-agent systems provide a powerful model framework for investigating evolutionary behaviour amongst animat agents. We have developed a microscopic animat-based model in which autonomous agents are microscopically controlled by a rule-set that can be evolved using suitable operators. Our system can support over a million animats co-existing over many generations and has already been used to explore several collective phenomena including clustering; segregation; tribal warfare and battlefront formation. We incorporate simple microscopic behaviour rules based on local view information that determine animat feeding; breeding; movement; seeking; and avoidance. We use a simple agent state model consisting of position, current health and age and a genetic code for each animat. We find a number of spatially-rich and complex, emergent patterns from the microscopic model and discuss how the model’s convergence to stable macroscopic behaviour cycles is related to the localized rule parameters.We illustrate how an animat agent population of predators and prey can evolve more effective individuals by applying genetic algorithms to the species rule-sets and how our model framework and approach can be applied to sociological and predatory phenomena.
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References
Adami, C.: On modeling life. In: Brooks, R., Maes, P. (eds.) Proc. Artificial Life IV, pp. 269–274. MIT Press, Cambridge (1994)
Adami, C.: Introduction to Artificial Life. Springer, Heidelberg (1998)
Bajec, I.L., Zimic, N., Mraz, M.: Fuzzifying the thoughts of animats. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 1103–1165. Springer, Heidelberg (2003)
Bonabeau, E., Theraulaz, G., Fourcassie, V., Deneubourg, J.L.: Phase-ordering kinetics of cemetery organization in ants. Phys. Rev. E 57, 4568–4571 (1998)
Dorigo, M., Maniezzo, V., Colomi, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics – Part B 26, 29–41 (1996)
Fogel, L.J.: On the organization of intellect. PhD thesis. University of California, Los Angeles (1964)
Franklin, S.: Artificial Minds. MIT Press, Cambridge (1997)
Guye-Vuilleme, A., Thalmann, D.: A high-level architecture for believable social agents. Virtual Reality 5, 95–106 (2000)
Hawick, K.A., Scogings, C.J., James, H.A.: Spatial emergence of genotypical tribes in an animat simulation model. In: Henderson, S.G., Biller, B., Hsieh, M.H., Tew, J.D., Barton, R.R. (eds.) Proc. 2007 Winter Simulation Conference 2007 (WSC 2007), Washington DC, USA, pp. 1216–1222 (2007)
Hawick, K.A., Scogings, C.J., James, H.A.: Defensive spiral emergence in a predator-prey model. Complexity International, 1–10 (2008)
Hinchey, M.G., Sterritt, R., Rouff, C.: Swarms and swarm intelligence. Computer, 111–113 (2007)
Holland, J.H.: Adaptation in natural and artificial systems, 2nd edn. MIT Press, Cambridge (1992)
Holland, J.H.: Echoing emergence: Objectives, rough definitions, and speculations for echo-class models. In: Cowan, G.A., Pines, D., Meltzer, D. (eds.) Complexity: Metaphors, Models and Reality, pp. 309–342. Addison-Wesley, Reading (1994)
Holland, J.H.: Hidden order: How adaptation builds complexity. Addison-Wesley, Reading (1995)
Kauffman, S.A.: Investigations. Oxford University Press, Oxford (2000)
Lenski, R.E., Ofira, C., Collier, T.C., Adami, C.: Genome complexity, robustness, and genetic interactions in digital organisms. Nature, 661–664 (1999)
Lotka, A.J.: Elements of Physical Biology. Williams and Wilkins, Baltimore (1925)
Mouret, J.B., Doncieux, S.: Incremental evolution of animats behaviors as a multi-objective optimization. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS (LNAI), vol. 5040, pp. 210–219. Springer, Heidelberg (2008)
Murray, J.D.: Mathematical Biology. Springer, Heidelberg (2001)
Rasmussen, S., Knudsen, C., Feldberg, R., Hindsholm, M.: The coreworld: Emergence and evolution of cooperative structures in a computational chemistry. Physica D 42, 11 (1990)
Ray, T.: An approach to the synthesis of life. In: Artificial Life II. Santa Fe Institute Studies in the Sciences of Complexity, vol. xi, pp. 371–408 (1991)
Reynolds, C.: Flocks, herds and schools: A distributed behavioral model. In: SIGRAPH 1987: Proc. 14th Annual Conf. on Computer Graphics and Interactive Techniques, pp. 25–34. ACM, New York (1987)
Romanczuk, P., Couzin, I.D., Schimansky-Geier, L.: Collective motion due to individual escape and pursuit response. Phys. Rev. Lett. 102, 010602–1–4 (2009)
Ronkko, M.: An artificial ecosystem: Emergent dynamics and lifelike properties. J. ALife 13, 159–187 (2007)
Ross, D., Brook, A., Thompson, D. (eds.): Dennett’s Philosophy: A Comprehensive Assessment. MIT Press, Cambridge (2000)
Scogings, C.J., Hawick, K.A., James, H.A.: Tools and techniques for optimisation of microscopic artificial life simulation models. In: Nyongesa, H. (ed.) Proceedings of the Sixth IASTED International Conference on Modelling, Simulation, and Optimization, Gabarone, Botswana, pp. 90–95 (2006)
Scogings, C.J., Hawick, K.A.: Global constraints and diffusion in a localised animat agent model. In: Proc. IASTED Int. Conf. on Applied Simulation and Modelling, Corfu, Greece, pp. 14–19 (2008)
Scogings, C., Hawick, K.: Altruism amongst spatial predator-prey animats. In: Bullock, S., Noble, J., Watson, R., Bedau, M. (eds.) Proc. 11th Int. Conf. on the Simulation and Synthesis of Living Systems (ALife XI), pp. 537–544. MIT Press, Winchester (2008)
Scogings, C., Hawick, K.: Pack-hunting multi-agent animats. In: Proc. IASTED Int. Symp. on Artificial Intelligence and Applications, Innsbruck, Austria, pp. 220–224 (2009)
Serugendo, G.D.M., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.): Engineering Self-Organising Systems. Springer, Heidelberg (2004)
Turney, J.: Frankenstein’s Footsteps - Science, Genetics and Popular Culture. Yale University Press, New Haven (1998)
Tyrrell, T., Mayhew, J.E.W.: Computer simulation of an animal environment. In: Meyer, J.A., Wilson, S.W. (eds.) From Animals to Animats 1: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pp. 263–272. MIT Press, Cambridge (1991)
Volterra, V.: Variazioni e fluttuazioni del numero d’individui in specie animali conviventi. Mem. R. Accad. Naz. dei Lincei, Ser VI 2 (1926)
Warren, M.S., Salmon, J.K.: A parallel hashed oct-tree n-body algorithm. In: Supercomputing, 12–21 (1993)
Watts, J.M.: Animats: computer-simulated animals in behavioural research. J. Anim. Sci. 76, 2596–2604 (1998)
Wilson, S.W.: The animat path to AI. In: Meyer, J.A., Wilson, S.W. (eds.) From Animals to Animats 1: Proceedings of The First International Conference on Simulation of Adaptive Behavior, pp. 15–21. MIT Press, Cambridge (1991)
Wurtz, R.P. (ed.): Organic Computing. Springer, Heidelberg (2008)
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Hawick, K.A., Scogings, C.J. (2010). Complex Emergent Behaviour from Evolutionary Spatial Animat Agents. In: Sarker, R.A., Ray, T. (eds) Agent-Based Evolutionary Search. Adaptation, Learning, and Optimization, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13425-8_7
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DOI: https://doi.org/10.1007/978-3-642-13425-8_7
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