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Complexity Measures in Automatic Design of Robot Swarms: An Exploratory Study

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Artificial Life and Evolutionary Computation (WIVACE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 830))

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Abstract

The design of control software for robot swarms is a challenging endeavour as swarm behaviour is the outcome of the entangled interplay between the dynamics of the individual robots and the interactions among them. Automatic design techniques are a promising alternative to classic ad-hoc design procedures and are especially suited to deal with the inherent complexity of swarm behaviours. In an automatic method, the design problem is cast into an optimisation problem: the solution space comprises instances of control software and an optimisation algorithm is applied to tune the free parameters of the architecture. Recently, some information theory and complexity theory measures have been proposed for the analysis of the behaviour of single autonomous agents; a similar approach may be fruitfully applied also to swarms of robots. In this work, we present a preliminary study on the applicability of complexity measures to robot swarm dynamics. The aim of this investigation is to compare and analyse prominent complexity measures when applied to data collected during the time evolution of a robot swarm, performing a simple stationary task. Although preliminary, the results of this study enable us to state that the complexity measures we used are able to capture relevant features of robot swarm dynamics and to identify typical patterns in swarm behaviour.

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Notes

  1. 1.

    This goal is motivated by a conjecture on the so-called reality gap, which has been advanced in [3, 10].

  2. 2.

    Indeed, due to excessive computational resources required, for this preliminary step we did not applied measures of complexity based on model construction, such as the ones by Crutchfield et al. [7].

  3. 3.

    Not to be confused with the excess entropy [26], which is defined for \(n \rightarrow \infty \).

  4. 4.

    The name comes from the name initials of its inventors.

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Acknowledgements

Andrea Roli acknowledges the support of Université libre de Bruxelles as visiting professor in the “Chaire internationale” programme. Mauro Birattari acknowledges support from the Belgian Fonds de la Recherche Scientifique – FNRS. The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 681872).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Campus of Cesena, Alma Mater Studiorum, Università di Bologna, Cesena, Italy

    Andrea Roli

  2. IRIDIA, Université libre de Bruxelles, Brussels, Belgium

    Antoine Ligot & Mauro Birattari

Authors
  1. Andrea Roli
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  2. Antoine Ligot
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  3. Mauro Birattari
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Corresponding author

Correspondence to Andrea Roli .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. Ca’ Foscari University of Venice, Venice, Italy

    Irene Poli

  3. University of Bologna, Cesena, Italy

    Andrea Roli

  4. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

  5. Ca’ Foscari University of Venice, Venice, Italy

    Debora Slanzi

  6. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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Roli, A., Ligot, A., Birattari, M. (2018). Complexity Measures in Automatic Design of Robot Swarms: An Exploratory Study. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2017. Communications in Computer and Information Science, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-319-78658-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-78658-2_18

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