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Higher-order Emergence in Collective AI Systems from Computational Model of Dictyostelium discoideum to Swarm Robotics

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Defense Thèse de doctorat : Univ. Genève, 2020 - SdS 141 - 2020/02/27
Abstract In this thesis, we show how to apply the method, the frameworks, and how to derive agent-based models of both first- and second-order emergence on the specific case of the social amoeba Dictyostelium discoideum. Overall, this thesis proposes a new design pattern “leader-follower”, describing a mechanism for achieving higher-order emergent behavior in artificial systems, derived from D.discoideum behavior. We eventually translate this pattern into swarms of Kilobots. In general, our computational simulations can replicate the behaviors of D. discoideum system it parallels and to do so based on the present, identified characteristics of the system from aggregation until slug formation. To achieve this goal, it is required to model different steps for each phase of the D. discoideum life cycle. Overcoming this challenge is possible by experimental1 and theoretical studies. These studies cover understanding the implications of the conceptual and main algorithmic steps of the model for each phase.
Keywords Bio-inspired Swarm ModelingMulti-agent SystemsSlime MoldDictyostelium discoideumSelf- organizationQuorum SensingUnicellular CommunicationKilobot
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URN: urn:nbn:ch:unige-1417669
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Thesis (90.6 MB) - public document Free access
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Project FNS: 205321 179023
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PARHIZKAR, Mohammad. Higher-order Emergence in Collective AI Systems from Computational Model of Dictyostelium discoideum to Swarm Robotics. Université de Genève. Thèse, 2020. doi: 10.13097/archive-ouverte/unige:141766 https://archive-ouverte.unige.ch/unige:141766

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Deposited on : 2020-09-23

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