Evolving autonomous agents with simulated brains using L2L and Netlogo

Yu, Jessica

Bachelor Thesis

FZJ-2022-00853

Evolving autonomous agents with simulated brains using L2L and Netlogo

Yu, J. (Corresponding author)FZJ*

2021
Jülich

Jülich 76 p (2021) = Bachelorarbeit, RWTH Aachen, 2021

Please use a persistent id in citations: http://hdl.handle.net/2128/30612

Abstract: Artificial neural networks (ANNs) are popular machine learning techniques used to model autonomous agents. Spiking neural networks (SNNs) provide the ability to reproduce spatio-temporal dynamics by transmitting information through action potentials or spikes. Given their more biologically realistic characteristic, they are particularly attractive for modelling biological systems, including the analysis and understanding of biological self organisation. As with many neural models, the difficulty in achieving the desired performance is finding the appropriate parameters settings. A commonly used autonomous approach is given by genetic algorithms (GAs), which provide an evolution-based search technique inspired by natural adaptation processes. The performance of these meta-heuristic search techniques depends on the settings of its hyperparameters, which present a challenging task on their own.In this work, a multi-agent simulation model embedded in NetLogo is investigated. It simulates an artificial ant navigating through a virtual maze with many obstacles in search of food. Through this process the ant is controlled by an SNN, whose parameter optimisation is examined and optimised in this thesis using GAs of two different tools (L2L and BehaviorSearch). Afterwards, a deeper investigation on the optimized SNNs is covered to understand and explain the observed behavior in the simulation.

Note: Bachelorarbeit, RWTH Aachen, 2021

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Appears in the scientific report 2021

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OpenAccess