Abstract
In this work we investigate the dynamical properties of the Boolean networks (BN) that control a robot performing a composite task. Initially, the robot must perform phototaxis, i.e. move towards a light source located in the environment; upon perceiving a sharp sound, the robot must switch to antiphototaxis, i.e. move away from the light source. The network controlling the robot is subject to an adaptive walk and the process is subdivided in two sequential phases: in the first phase, the learning feedback is an evaluation of the robot’s performance in achieving only phototaxis; in the second phase, the learning feedback is composed of a performance measure accounting for both phototaxis and antiphototaxis. In this way, it is possible to study the properties of the evolution of the robot when its behaviour is adapted to a new operational requirement. We analyse the trajectories followed by the BNs in the state space and find that the best performing BNs (i.e. those able to maintaining the previous learned behaviour while adapting to the new task) are characterised by generalisation capabilities and the emergence of simple behaviours that are dynamically combined to attain the global task. In addition, we also observe a further remarkable property: the complexity of the best performing BNs increases during evolution. This result may provide useful indications for improving the automatic design of robot controllers and it may also help shed light on the relation and interplay among robustness, evolvability and complexity in evolving systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aldana, M., Balleza, E., Kauffman, S., Resendiz, O.: Robustness and evolvability in genetic regulatory networks. Journal of Theoretical Biology 245, 433–448 (2007)
Bertschinger, N., Natschläger, T.: Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation 16, 1413–1436 (2004)
Crutchfield, J.: The calculi of emergence: Computation, dynamics, and induction. Physica D 75, 11–54 (1994)
Crutchfield, J., Young, K.: Computation at the onset of chaos. In: Complexity, Entropy, and Physics of Information. Addison Wesley (1990)
Edlund, J., Chaumont, N., Hintze, A., Koch, C., Tononi, G., Adami, C.: Integrated information increases with fitness in the evolution of animats. PLOS Computational Biology 7(10), e1002236:1–e1002236:13 (2011)
Garattoni, L., Roli, A., Amaducci, M., Pinciroli, C., Birattari, M.: Boolean network robotics as an intermediate step in the synthesis of finite state machines for robot control. In: Liò, P., Miglino, O., Nicosia, G., Nolfi, S., Pavone, M. (eds.) Advances in Artificial Life, ECAL 2013, pp. 372–378. The MIT Press (2013)
Gell-Mann, M., Lloyd, S.: Information measures, effective complexity, and total information. Complexity 2(1), 44–52 (1996)
Grassberger, P.: Randomness, information, and complexity, August 2012. arXiv:1208.3459
Hordijk, W.: The EvCA project: A brief history. Complexity 18, 15–19 (2013)
Joshi, N., Tononi, G., Koch, C.: The minimal complexity of adapting agents increases with fitness. PLOS Computational Biology 9(7), e1003111:1–e1003111:10 (2013)
Kauffman, S.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, UK (1993)
Kinouchi, O., Copelli, M.: Optimal dynamical range of excitable networks at criticality. Nature Physics 2, 348–351 (2006)
Langton, C.: Computation at the edge of chaos: Phase transitions and emergent computation. Physica D 42, 12–37 (1990)
Legenstein, R., Maass, W.: Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20, 323–334 (2007)
Lopez-Ruiz, R., Mancini, H., Calbet, X.: A statistical measure of complexity. Physics Letters A 209, 321–326 (1995)
Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J.C., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: Gonçalves, P., Torres, P., Alves, C. (eds.) Proceedings of the 9th Conference on Autonomous Robot Systems and Competitions, vol. 1, pp. 59–65 (2009)
Nykter, M., Price, N., Aldana, M., Ramsey, S., Kauffman, S., Hood, L., Yli-Harja, O., Shmulevich, I.: Gene expression dynamics in the macrophage exhibit criticality. In: Proceedings of the National Academy of Sciences, USA, vol. 105, pp. 1897–1900 (2008)
Packard, N.: Adaptation toward the edge of chaos. In: Dynamic Patterns in Complex Systems, pp. 293–301 (1988)
Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Birattari, M., Gambardella, L., Dorigo, M.: ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. Swarm Intelligence 6(4), 271–295 (2012)
Prokopenko, M., Boschetti, F., Ryan, A.: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11–28 (2008)
Ribeiro, A., Kauffman, S., Lloyd-Price, J., Samuelsson, B., Socolar, J.: Mutual information in random Boolean models of regulatory networks. Physical Review E 77, 011901:1–011901:10 (2008)
Roli, A., Manfroni, M., Pinciroli, C., Birattari, M.: On the design of boolean network robots. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcázar, A.I., Merelo, J.J., Neri, F., Preuss, M., Richter, H., Togelius, J., Yannakakis, G.N. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 43–52. Springer, Heidelberg (2011)
Roli, A., Villani, M., Serra, R., Garattoni, L., Pinciroli, C., Birattari, M.: Identification of dynamical structures in artificial brains: an analysis of boolean network controlled robots. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 324–335. Springer, Heidelberg (2013)
Serra, R., Villani, M.: Modelling bacterial degradation of organic compounds with genetic networks. Journal of Theoretical Biology 189(1), 107–119 (1997)
Serra, R., Villani, M., Barbieri, A., Kauffman, S., Colacci, A.: On the dynamics of random Boolean networks subject to noise: Attractors, ergodic sets and cell types. Journal of Theoretical Biology 265(2), 185–193 (2010)
Serra, R., Villani, M., Semeria, A.: Genetic network models and statistical properties of gene expression data in knock-out experiments. Journal of Theoretical Biology 227, 149–157 (2004)
Shalizi, C.: Methods and techniques of complex systems science: An overview, March 2006. arXiv:nlin/0307015
Shmulevich, I., Dougherty, E.: Probabilistic Boolean Networks: The Modeling and Control of Gene Regulatory Networks. SIAM, Philadelphia (2009)
Shmulevich, I., Kauffman, S., Aldana, M.: Eukaryotic cells are dynamically ordered or critical but not chaotic. PNAS 102, 13439–13444 (2005)
Strogatz, S.: Nonlinear dynamics and chaos. Perseus Books Publishing (1994)
Villani, M., Serra, R.: On the dynamical properties of a model of cell differentiation. EURASIP Journal on Bioinformatics and Systems Biology 4, 1–8 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Roli, A., Villani, M., Serra, R., Benedettini, S., Pinciroli, C., Birattari, M. (2015). Dynamical Properties of Artificially Evolved Boolean Network Robots. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds) AI*IA 2015 Advances in Artificial Intelligence. AI*IA 2015. Lecture Notes in Computer Science(), vol 9336. Springer, Cham. https://doi.org/10.1007/978-3-319-24309-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-24309-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24308-5
Online ISBN: 978-3-319-24309-2
eBook Packages: Computer ScienceComputer Science (R0)