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Coevolutionary learning of swarm behaviors without metrics

Published: 12 July 2014 Publication History

Abstract

We propose a coevolutionary approach for learning the behavior of animals, or agents, in collective groups. The approach requires a replica that resembles the animal under investigation in terms of appearance and behavioral capabilities. It is able to identify the rules that govern the animals in an autonomous manner. A population of candidate models, to be executed on the replica, compete against a population of classifiers. The replica is mixed into the group of animals and all individuals are observed. The fitness of the classifiers depends solely on their ability to discriminate between the replica and the animals based on their motion over time. Conversely, the fitness of the models depends solely on their ability to 'trick' the classifiers into categorizing them as an animal. Our approach is metric-free in that it autonomously learns how to judge the resemblance of the models to the animals. It is shown in computer simulation that the system successfully learns the collective behaviors of aggregation and of object clustering. A quantitative analysis reveals that the evolved rules approximate those of the animals with a good precision.

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Coevolutionary Learning of Swarm Behaviors Without Metrics - Video Attachment Contact Information: The authors are with the Sheffield Centre for Robotics and the Department of Automatic Control and Systems Engineering, The University of Sheffield, UK. Lab Homepage: http://naturalrobotics.group.shef.ac.uk, Emails: {wei.li11, m.gauci, r.gross}@sheffield.ac.uk

References

[1]
H.-G. Beyer. The Theory of Evolution Strategies. Springer, Berlin, Heidelberg, Germany, 2001.
[2]
J. J. Bolhuis and L.-A. Giraldeau. The Behavior of Animals: Mechanisms, Function, and Evolution. Wiley, Hoboken, NJ, 2004.
[3]
J. Bongard and H. Lipson. Automated robot function recovery after unanticipated failure or environmental change using a minimum of hardware trials. In Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware, pages 169--176, Seattle, WA, June 2004.
[4]
J. Bongard and H. Lipson. Nonlinear system identification using coevolution of models and tests. IEEE Transactions on Evolutionary Computation, 9(4):361--384, 2005.
[5]
S. Camazine et al. Self-Organization in Biological Systems. Princeton University Press, Princeton, NJ, 2001.
[6]
C. R. Carroll and D. H. Janzen. Ecology of foraging by ants. Annual Review of Ecology and Systematics, 4:231--257, 1973.
[7]
J. L. Elman. Finding structure in time. Cognitive Sci., 14(2):179--211, 1990.
[8]
D. Floreano and C. Mattiussi. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press, Cambridge, MA, 2008.
[9]
M. Gauci, J. Chen, W. Li, T. J. Dodd, and R. Groß. Clustering objects with robots that do not compute. In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems, IFAAMAS, Richland, SC. In press.
[10]
M. Gauci, J. Chen, W. Li, T. J. Dodd, and R. Groß. Self-organized aggregation without computation. International Journal of Robotics Research. In press.
[11]
A. Gribovskiy, J. Halloy, J.-L. Deneubourg, H. Bleuler, and F. Mondada. Towards mixed societies of chickens and robots. In Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4722--4728, Boston, MA, October 2010.
[12]
J. Halloy et al. Social integration of robots into groups of cockroaches to control self-organized choices. Science, 318(5853):1155--1158, 2007.
[13]
J. Halloy, F. Mondada, S. Kernbach, and T. Schmickl. Towards bio-hybrid systems made of social animals and robots. In Biomimetic and Biohybrid Systems, volume 8064 of Lecture Notes in Computer Science, pages 384--386. Springer, Berlin, Heidelberg, Germany, 2013.
[14]
R. Jeanson et al. Self-organized aggregation in cockroaches. Animal Behaviour, 69(1):169--180, 2005.
[15]
R. D. King et al. The automation of science. Science, 324(5923):85--89, 2009.
[16]
S. Koos, J. Mouret, and S. Doncieux. Automatic system identification based on coevolution of models and tests. In Proceedings of 2009 IEEE Congress on Evolutionary Computation, pages 560--567, Trondheim, Norway, May 2009.
[17]
B. Kouchmeshky, W. Aquino, J. Bongard, and H. Lipson. Co-evolutionary algorithm for structural damage identification using minimal physical testing. International Journal for Numerical Methods in Engeering, 69(5):1085--1107, 2007.
[18]
J. Krause, A. F. Winfield, and J.-L. Deneubourg. Interactive robots in experimental biology. Trends in Ecology and Evolution, 26(7):369--375, 2011.
[19]
W. Li, M. Gauci, and R. Groß. A coevolutionary approach to learn animal behavior through controlled interaction. In Proceedings of the 15th Annual Conference of Genetic and Evolutionary Computation, pages 223--230, Amsterdam, Netherlands, July 2013.
[20]
D. Ly and H. Lipson. Optimal experiment design for coevolutionary active learning. IEEE Transactions on Evolutionary Computation, PP(99):1--11, 2013.
[21]
S. Magnenat, M. Waibel, and A. Beyeler. Enki: The fast 2D robot simulator, 2011.
[22]
J.-A. Meyer and A. Guillot. Biologically inspired robots. In B. Siciliano and O. Khatib, editors, Springer Handbook of Robotics, Springer Handbooks, pages 1395--1422. Springer, Berlin, Heidelberg, Germany, 2008.
[23]
F. Mondada, M. Bonani, X. Raemy, J. Pugh, C. Canci, A. Klaptocz, S. Magnenat, J.-C. Zufferey, D. Floreano, and A. Martinoli. The e-puck, a robot designed for education in engineering. In Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, volume 1, pages 59--65, 2009.
[24]
T. Schmickl et al. Assisi: Mixing animals with robots in a hybrid society. In Biomimetic and Biohybrid Systems, volume 8064 of Lecture Notes in Computer Science, pages 441--443. Springer, Berlin, Heidelberg, Germany, 2013.
[25]
M. Schmidt and H. Lipson. Distilling free-form natural laws from experimental data. Science, 324(5923):81--85, 2009.
[26]
W. J. Sutherland. The importance of behavioural studies in conservation biology. Animal Behaviour, 56(4):801--809, 1998.
[27]
R. Vaughan, N. Sumpter, J. Henderson, A. Frost, and S. Cameron. Experiments in automatic flock control. Robotics and Autonomous Systems, 31(1):109--117, 2000.

Cited By

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  • (2018)Modelling Human Movements With Turing Learning2018 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2018.8628691(2254-2261)Online publication date: Nov-2018
  • (2016)Turing learning: a metric-free approach to inferring behavior and its application to swarmsSwarm Intelligence10.1007/s11721-016-0126-110:3(211-243)Online publication date: 30-Aug-2016

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cover image ACM Conferences
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
July 2014
1478 pages
ISBN:9781450326629
DOI:10.1145/2576768
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2014

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Author Tags

  1. animal behavior
  2. artificial life
  3. coevolution
  4. evolutionary robotics
  5. swarm robotics
  6. system identification

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GECCO '14
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GECCO '14: Genetic and Evolutionary Computation Conference
July 12 - 16, 2014
BC, Vancouver, Canada

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2018)Modelling Human Movements With Turing Learning2018 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2018.8628691(2254-2261)Online publication date: Nov-2018
  • (2016)Turing learning: a metric-free approach to inferring behavior and its application to swarmsSwarm Intelligence10.1007/s11721-016-0126-110:3(211-243)Online publication date: 30-Aug-2016

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