sc 16(3): e5

Research Article

Evolving Mixed Societies: A one-dimensional modelling approach

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  • @ARTICLE{10.4108/eai.3-12-2015.2262514,
        author={Michael Bodi and Martina Szopek and Payam Zahadat and Thomas Schmickl},
        title={Evolving Mixed Societies: A one-dimensional modelling approach},
        journal={EAI Endorsed Transactions on Smart Cities},
        keywords={mixed-societies, bio-hybrid systems, evolutionary computation, multi agent systems, swarm robotics},
  • Michael Bodi
    Martina Szopek
    Payam Zahadat
    Thomas Schmickl
    Year: 2016
    Evolving Mixed Societies: A one-dimensional modelling approach
    DOI: 10.4108/eai.3-12-2015.2262514
Michael Bodi1,*, Martina Szopek1, Payam Zahadat1, Thomas Schmickl1
  • 1: Artificial Life Lab of the Department of Zoology, Karl-Franzens University Graz, Austria
*Contact email:


Natural self-organising collective systems like social insect societies are often used as a source of inspiration for robotic applications. In return, developing such self-organising robotic systems can lead to a better understanding of natural collective systems. By unifying the communication channels of the natural and artificial agents these two collective systems can be merged into one bio-hybrid society. In this work we demonstrate the feasibility of such a bio-hybid society by introducing a simple one-dimensional model. A set of patches forms a one-dimensional arena, each patch represents a stationary robot, which is controlled by an AHHS (Artificial Homeostatic Hormone System) control software. The stationary robots are able to produce different types of environmental stimuli. Simulated bees react diversely to the different stimuli types. An evolutionary computation algorithm changes the properties of the AHHS and defines the interactions between the robots and their properties of stimuli emission. The task is an aggregation of simulated bees at a predefined aggregation spot. We demonstrate that an evolved AHHS is a very feasible tool for controlling these stationary robots. Furthermore we show that an AHHS even works robustly in different setups and dynamic environments even though the controller was not specially evolved for these purposes.