Abstract
A novel online adaptation mechanism has been recently introduced, which is inspired by the phenotypic plasticity property present in biological organisms and which exploits the intrinsic computational capabilities of the Boolean network (BN) controlling the robot. In these robots, the BN is coupled with sensors and actuators and plays the role of the control system. The coupling is dynamically changed so as to increase a utility function. Recent results have shown that this mechanism can yield robots accomplishing tasks of a different nature and that, in general, critical networks attain the best performance compared to the other ones.
An analysis of this mechanism in noisy environments is needed to assess its performance in more realistic scenarios and to reduce the so-called reality gap. This work aims precisely to start investigating this question and to generalise the previously attained result.
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
References
Bonani, M., et al.: The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 18–22 October 2010, pp. 4187–4193. Taipei, Taiwan. IEEE (2010)
Braccini, M., Roli, A., Kauffman, S.: Online adaptation in robots as biological development provides phenotypic plasticity. arXiv preprint arXiv:2006.02367 (2020). To be published in Proceedings of WIVACE 2021, LNCS Series, Springer
Braccini, M., Roli, A., Barbieri, E., Kauffman, S.A.: On the criticality of adaptive Boolean network robots. Entropy 24(10), 1368 (2022). https://doi.org/10.3390/e24101368
Braccini, M., Roli, A., Villani, M., Serra, R.: Dynamical properties and path dependence in a gene-network model of cell differentiation. Soft. Comput. 25(9), 6775–6787 (2020). https://doi.org/10.1007/s00500-020-05354-0
Braitenberg, V.: Vehicles: Experiments in Synthetic Psychology. MIT Press, Cambridge (1986)
Cariani, P.: To evolve an ear. Epistemological implications of Gordon Pask’s electrochemical devices. Syst. Res. 10(3), 19–33 (1993)
Fusco, G., Minelli, A.: Phenotypic plasticity in development and evolution: facts and concepts. Philos. Trans. R. Soc. B 365, 547–556 (2010)
Huang, S., Eichler, G., Bar-Yam, Y., Ingber, D.: Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94(128701), 1–4 (2005)
Huang, S., Ernberg, I., Kauffman, S.: Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. In: Seminars in Cell and Developmental Biology, vol. 20, pp. 869–876. Elsevier (2009)
Kauffman, S.: Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22(3), 437–467 (1969)
Kelly, S., Panhuis, T., Stoehr, A.: Phenotypic plasticity: molecular mechanisms and adaptive significance. Compr. Physiol. 2(2), 1417–1439 (2011)
Kolchinsky, A., Wolpert, D.: Semantic information, autonomous agency and non-equilibrium statistical physics. Interface Focus 8(6), 20180041 (2018)
Luque, B., Solé, R.: Phase transitions in random networks: simple analytic determination of critical points. Phys. Rev. E 55(1), 257 (1997)
Montagna, S., Braccini, M., Roli, A.: The impact of self-loops on Boolean networks attractor landscape and implications for cell differentiation modelling. IEEE ACM Trans. Comput. Biol. Bioinform. 18(6), 2702–2713 (2021). https://doi.org/10.1109/TCBB.2020.2968310
Pinciroli, C., et al.: ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. Swarm Intell. 6(4), 271–295 (2012)
Roli, A., Villani, M., Serra, R., Benedettini, S., Pinciroli, C., Birattari, M.: Dynamical properties of artificially evolved Boolean network robots. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds.) AI*IA 2015. LNCS (LNAI), vol. 9336, pp. 45–57. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24309-2_4
Roli, A., Benedettini, S., Birattari, M., Pinciroli, C., Serra, R., Villani, M.: A preliminary study on BN-robots’ dynamics. In: Proceedings of the Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE 2012), pp. 1–4 (2012)
Roli, A., Braccini, M.: Attractor landscape: a bridge between robotics and synthetic biology. Complex Syst. 27, 229–248 (2018). https://doi.org/10.25088/ComplexSystems
Roli, A., Manfroni, M., Pinciroli, C., Birattari, M.: On the design of Boolean network robots. In: Di Chio, C., et al. (eds.) EvoApplications 2011. LNCS, vol. 6624, pp. 43–52. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20525-5_5
Serra, R., Villani, M., Graudenzi, A., Kauffman, S.: Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data. J. Theor. Biol. 246(3), 449–460 (2007). https://doi.org/10.1016/j.jtbi.2007.01.012
Villani, M., Barbieri, A., Serra, R.: A dynamical model of genetic networks for cell differentiation. PLoS ONE 6(3), e17703 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Braccini, M., Barbieri, E., Roli, A. (2023). The Role of Dynamical Regimes of Online Adaptive BN-Robots in Noisy Environments. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-31183-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31182-6
Online ISBN: 978-3-031-31183-3
eBook Packages: Computer ScienceComputer Science (R0)