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Evolutionary computational synthesis of self-organizing systems

Published online by Cambridge University Press:  22 July 2014

James Humann ,
Newsha Khani  and
Yan Jin
James Humann
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
Newsha Khani
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
Yan Jin*
Affiliation:
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, California, USA
*
Reprint requests to: Yan Jin, Department of Aerospace and Mechanical Engineering, University of Southern California, 3650 McClintock Avenue, OHE430, Los Angeles, CA 90089-1453, USA. E-mail: yjin@usc.edu
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Abstract

A computational approach for the design of self-organizing systems is proposed that employs a genetic algorithm to efficiently explore the vast space of possible configurations of a given system description. To generate the description of the system, a two-field based model is proposed in which agents are assigned parameterized responses to two “fields,” a task field encompassing environmental features and task objects, and a social field arising from agent interactions. The aggregate effect of these two fields, sensed by agents individually, governs the behavior of each agent, while the system-level behavior emerges from the actions of and interactions among the agents. Task requirements together with performance preferences are used to compose system fitness functions for evolving functional and efficient self-organizing mechanisms. Case studies on the evolutionary synthesis of self-organizing systems are presented and discussed. These case studies focus on achieving system-level behavior with minimal explicit coordination among agents. Agents were able to collectively display flocking, exploration, and foraging through self-organization. The proposed two-field model was able to capture important features of self-organizing systems, and the genetic algorithm was able to generate self-organizing mechanisms by which agents could form task-based structures to fulfill functional requirements.

Keywords

Agent-Based ApproachComputational DesignDesign SynthesisGenetic AlgorithmSelf-Organization
Type
Research Article
Information
AI EDAM , Volume 28 , Issue 3: Computational Design Synthesis , August 2014 , pp. 259 - 275
Copyright
Copyright © Cambridge University Press 2014 

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References

REFERENCES

Ashby, W.R. (1958). Requisite variety and its implications for the control of complex systems. Cybernetica, 1(2) 83–99.Google Scholar
Bai, L., & Bree, D. (2012). Chemotaxis-inspired cellular primitives for self-organizing shape formation. In Morphogenetic Engineering: Toward Programmable Complex Systems (Doursat, R., Sayama, H., & Michel, O., Eds.), pp. 141–156. Berlin: Springer–Verlag.Google Scholar
Beckers, R., Holl, O.E., Deneubourg, J.L., Bielefeld, Z., & Bielefeld, D. (1994). From Local Actions to Global Tasks: Stigmergy and Collective Robotics, pp. 181–189. Cambridge, MA: MIT Press.Google Scholar
Beni, G. (1988). The concept of cellular robotic system. Proc. IEEE Int. Symp. Intelligent Control, 1988, pp. 57–62.Google Scholar
Bentley, P. (1999). Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem. Proc. Genetic and Evolutionary Computation Conf., pp. 35–43. San Francisco, CA: Morgan Kaufmann.Google Scholar
Bentley, P. (2001). Digital Biology: How Nature Is Transforming Our Technology and Our Lives. New York: Simon & Schuster.Google Scholar
Calvez, B., & Hutzler, G. (2006). Automatic tuning of agent-based models using genetic algorithms. In Multi-Agent-Based Simulation VI (Sichman, J., & Antunes, L., Eds.), Vol. 3891, pp. 41–57. Berlin: Springer.Google Scholar
Chen, C., & Jin, Y. (2011). A behavior based approach to cellular self-organizing systems design. Proc. ASME 2011 Int. Design Engineering Technical Conf. & Computers and Information in Engineering Conf., Washington, DC.Google Scholar
Chiang, W. (2012). A meta-interaction model for designing cellular self-organizing systems. PhD Thesis. University of Southern California, Los Angeles.Google Scholar
Chiang, W., & Jin, Y. (2011). Toward a meta-model of behavioral interaction for designing complex adaptive systems. Proc. ASME 2011 Int. Design Engineering Technical Conf. & Computers and Information in Engineering Conf., pp. 1077–1088, Washington, DC.Google Scholar
Crutchfield, J.P., Mitchell, M., & Das, R. (1996). Evolving cellular automata with genetic algorithms: a review of recent work. Proc. 1st Int. Conf. Evolutionary Computation and Its Applications, Moscow.Google Scholar
Cucker, F., & Smale, S. (2007). Emergent behavior in flocks. IEEE Transactions on Automatic Control 52(5), 852–862.CrossRefGoogle Scholar
Doursat, R. (2011). The myriads of Alife: importing complex systems and self-organization into engineering. Proc. 2011 IEEE Symposium on Artificial Life, ALIFE, pp. 1–8.Google Scholar
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, 1st ed.Reading, MA: Addison–Wesley Professional.Google Scholar
Goldberg, D. (2002). The Design of Innovation, 1st ed.Berlin: Springer.Google Scholar
Goldstein, S., & Mowry, T. (2004). Claytronics: a scalable basis for future robots. Proc. Robosphere. Mountain View, CA: NASA Ames Research Center.Google Scholar
Haken, H. (1978). Synergetics: An Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology. Berlin: Springer–Verlag.Google Scholar
Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press.Google Scholar
Humann, J., & Jin, Y. (2013). Evolutionary design of cellular self-organizing systems. Proc. ASME 2013 Int. Design Engineering Technical Conf. Computers and Information in Engineering Conf., Portland, OR.Google Scholar
Jin, Y., & Chen, C. (2012). Field based behavior regulation for self-organization in cellular systems. Proc. Design Computing and Cognition Conf. DCC'12.Google Scholar
Jin, Y., & Li, W. (2007). Design concept generation: a hierarchical coevolutionary approach. Journal of Mechanical Design 129(10), 1012.Google Scholar
Kelly, K. (1994). Out of Control: The New Biology of Machines, Social Systems and the Economic World. Reading, MA: Addison–Wesley.Google Scholar
Korb, J. (2011). Termite mound architecture, from function to construction. In Biology of Termites: A Modern Synthesis (Bigness, D.E., Roisin, Y., & Lo, N., Eds.), pp. 349–373. Dordrecht: Springer.Google Scholar
Mitchell, M., Crutchfield, P., & Hraber, P. (1994). Evolving cellular automata to perform computations: mechanisms and impediments. Physica D: Nonlinear Phenomena 75(1–3), 361–391.Google Scholar
Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Proc. SPIE 4195, Mobile Robots XV and Telemanipulator and Telepresence Technologies VII, p. 67.Google Scholar
Reynolds, C.W. (1987). Flocks, herds, and schools: a distributed behavioral model. ACM SIGGRAPH Conf. Proc., pp. 25–34.Google Scholar
Rogers, C. (2014, February 3). “U.S. to propose vehicle-to-vehicle, crash-avoidance systems.” Wall Street Journal. Accessed at http://online.wsj.com/news/articles/SB10001424052702303942404579360972335289080Google Scholar
Rubenstein, M., Ahler, C., & Nagpal, R. (2012). Kilobot: a low cost scalable robot system for collective behaviors. Proc. 2012 IEEE Int. Conf. Robotics and Automation (ICRA), pp. 3293–3298.Google Scholar
Sadjadi, F. (2004). Comparison of fitness scaling functions in genetic algorithms with applications to optical processing. In Optical Science and Technology: The SPIE 49th Annual Meeting, pp. 356–364. Denver, CO: International Society for Optics and Photonics.Google Scholar
Shen, W.-M., Salemi, B., & Will, P. (2002). Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots. IEEE Transactions on Robotics and Automation 18(5), 700–712.Google Scholar
Song, Y., Kim, J.-H., & Shell, D. (2012). Self-organized clustering of square objects by multiple robots. In Swarm Intelligence (Dorigo, M., Birattari, M., Blum, C., Christensen, A., Engelbrecht, A., Groß, R., & Stützle, T., Eds.), Vol. 7461, pp. 308–315. Berlin: Springer.Google Scholar
Sridharan, P., & Campbell, M.I. (2005). A study on the grammatical construction of function structures. Artificial Intelligence for Engineering, Design Analysis and Manufacturing 19(3), 139–160.Google Scholar
Stonedahl, F., & Wilensky, U. (2010). Finding forms of flocking: evolutionary search in ABM parameter-spaces. Proc. MABS Workshop, 9th Int. Conf. Autonomous Agents and Multi-Agent Systems.Google Scholar
Thompson, J. (1967). Organizations in Action. New York: McGraw–Hill.Google Scholar
Trianni, V. (2008). Evolutionary Swarm Robotics: Evolving Self-Organising Behaviours in Groups of Autonomous Robots. Berlin: Springer.Google Scholar
Ueyama, T., Fukuda, T., & Arai, F. (1992). Structure configuration using genetic algorithm for cellular robotic system. Proc. IEEE/RSJ Int. Conf. Intelligent Systems, Vol. 3, p. 1542.Google Scholar
Van Berkel, S., Turi, D., Pruteanu, A., & Dulman, S. (2012). Automatic discovery of algorithms for multi-agent systems. Proc. 14th Int. Conf. Genetic and Evolutionary Computation Conf. Companion, pp. 337–344. New York: ACM.Google Scholar
Werfel, J. (2012). Collective construction with robot swarms. In Morphogenetic Engineering: Toward Programmable Complex Systems (Doursat, R., Sayama, H., & Michel, O., Eds.), pp. 115–140. Berlin: Springer–Verlag.Google Scholar
Werfel, J., & Nagpal, R. (2006). Extended stigmergy in collective construction. Intelligent Systems, IEEE 21(2), 20–28.Google Scholar
Wilensky, U. (1998 a). NetLogo. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling. Accessed at http://ccl.northwestern.edu/netlogoGoogle Scholar
Wilensky, U. (1998 b). NetLogo Flocking Model. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling. Accessed at http://ccl.northwestern.edu/netlogo/models/FlockingGoogle Scholar
Yogev, O., Shapiro, A.A., & Antonsson, E.K. (2008). Engineering by fundamental elements of evolution. Proc. ASME 2008 Int. Design Engineering Technical Conf., IDETC/CIE.Google Scholar
Zouein, G., Chen, C., & Jin, Y. (2010). Create adaptive systems through “DNA” guided cellular formation. Proc. Design Creativity 2010.Google Scholar