Abstract
Emergent agents are a promising approach to handle complex systems. Agent intelligence is thereby either defined by the number of states and the state transition function or the length of their steering programs. Evolution has shown to be successful in creating desired behaviors for such agents. Genetic algorithms have been used to find agents with fixed numbers of states and genetic programming is able to balance between the steering program length and the costs for longer programs. This paper extends previous work by further discussing the relationship between either using more agents with less intelligence or using fewer agents with higher intelligence. Therefore, the Creatures’ Exploration Problem with a complex input set is solved by evolving emergent agents. It shows that neither a sole increase in intelligence nor amount is the best solution. Instead, a cautious balance creates best results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Anderson, C.: Creation of desirable complexity: strategies for designing selforganized systems. In: Complex Engineered Systems, pp. 101–121. Perseus Books Group (2006)
Beckers, R., Deneubourg, J.L., Goss, S.: Trails and U-turns in the selection of the shortest path by the ant Lasius Niger. Journal of Theoretical Biology 159, 397–415 (1992)
Branke, J., Schmeck, H.: Evolutionary design of emergent behavior. In: Organic Computing, March 2008, pp. 123–140. Springer, Heidelberg (2008), http://www.springer.com/978-3-540-77656-7
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K.: A robust evolutionary framework for multi-objective optimization. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pp. 633–640. ACM, New York (2008)
Edmonds, B.: Using the experimental method to produce reliable self-organised systems. In: Brueckner, S.A., Di Marzo Serugendo, G., Karageorgos, A., Nagpal, R. (eds.) ESOA 2005. LNCS (LNAI), vol. 3464, pp. 84–99. Springer, Heidelberg (2005)
Goss, S., Aron, S., Deneubourg, J., Pasteels, J.: Self-organized shortcuts in the argentine ant. Naturwissenschaften 76(12), 579–581 (1989), http://dx.doi.org/10.1007/BF00462870
Halbach, M.: Algorithmen und Hardwarearchitekturen zur optimierten Aufzählung von Automaten und deren Einsatz bei der Simulation künstlicher Kreaturen. Ph.D. thesis, Technische Universität Darmstadt (2008)
Halbach, M., Heenes, W., Hoffmann, R., Tisje, J.: Optimizing the behavior of a moving creature in software and in hardware. In: Sloot, P.M.A., Chopard, B., Hoekstra, A.G. (eds.) ACRI 2004. LNCS, vol. 3305, pp. 841–850. Springer, Heidelberg (2004)
Halbach, M., Hoffmann, R., Both, L.: Optimal 6-state algorithms for the behavior of several moving creatures. In: El Yacoubi, S., Chopard, B., Bandini, S. (eds.) ACRI 2006. LNCS, vol. 4173, pp. 571–581. Springer, Heidelberg (2006)
Komann, M., Ediger, P., Fey, D., Hoffmann, R.: On the effectiveness of evolution compared to time-consuming full search of optimal 6-state automata. In: Vanneschi, L., Gustafson, S., Moraglio, A., De Falco, I., Ebner, M. (eds.) EuroGP 2009. LNCS, vol. 5481, pp. 280–291. Springer, Heidelberg (2009)
Komann, M., Fey, D.: Realising emergent image preprocessing tasks in cellular-automaton-alike massively parallel hardware. International Journal of Parallel, Emergent and Distributed Systems 22(2), 79–89 (2007)
Komann, M., Fey, D.: Evaluating the evolvability of emergent agents with different numbers of states. In: GECCO, pp. 1777–1778. ACM, New York (2009)
Koza, J.R.: Scalable learning in genetic programming using automatic function definition, pp. 99–117 (1994)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs, 3rd edn. Springer, London (1996)
Müller-Schloer, C., Sick, B.: Emergence in Organic Computing systems: Discussion of a controversial concept. In: Yang, L.T., Jin, H., Ma, J., Ungerer, T. (eds.) ATC 2006. LNCS, vol. 4158, pp. 1–16. Springer, Heidelberg (2006)
Roska, T., Chua, L.: The cnn universal machine: an analogic array computer. IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 40(3), 163–173 (1993)
Wolf, T.D., Holvoet, T.: Emergence versus self-organisation: Different concepts but promising when combined. In: Brueckner, S.A., Di Marzo Serugendo, G., Karageorgos, A., Nagpal, R. (eds.) ESOA 2005. LNCS (LNAI), vol. 3464, pp. 1–15. Springer, Heidelberg (2005)
Wolfram, S.: A New Kind of Science. Wolfram Media Inc., Champaign (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Komann, M., Fey, D. (2010). Revising the Trade-off between the Number of Agents and Agent Intelligence. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-12239-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12238-5
Online ISBN: 978-3-642-12239-2
eBook Packages: Computer ScienceComputer Science (R0)