Using Compression to Discover Interesting Behaviours in a Hybrid Braitenberg Vehicle

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The simple rules that govern the interactions between the different components of a complex system often lead to interesting behaviours that are unexpected. The experiments described in this paper involved creating a variation of a traditional Braitenberg Vehicle, by placing sensors on ten different possible locations on a simulated vehicle, and incorporating an obstacle avoidance behaviour using a subsumption-like architecture, which resulted in different unusual and unexpected behaviours being produced. The vehicle was allowed to explore a simulated environment which contained a single bright light in the centre with walls on the border. By using a novel combination of the Prediction by Partial Matching compression algorithm and k-means clustering, interesting emergent behaviours were effectively discovered within a search space of over 10,000 simulations produced from a simple interaction of light and proximity sensors on a vehicle and a single light source. The clustering algorithm discovered five distinct behaviours: circling and spiralling behaviours; interesting behaviours creating intricate rose petal-like structures; behaviours that create simple rose petal-like structures; behaviours with large movements and low complexity; and behaviours with less movement. The novel algorithm demonstrated in this paper has useful potential in the science of complex systems and modelling to help expedite the systematic exploration of a substantial search space of simulations in order to discover interesting behaviours.

Keywords

  • Sensors, Robots, Robot sensing systems, Wheels, Task analysis, Complex systems, Temperature sensors, Entropy coding, robot motion, Braitenberg vehicles, subsumption architecture, prediction by partial matching
Original languageEnglish
Pages (from-to)11316-11327
Number of pages12
JournalIEEE ACCESS
Volume9
Early online date11 Jan 2021
DOIs
Publication statusPublished - 21 Jan 2021
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