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Evolution of cooperation with partner choice in collective adaptive systems

Abstract : The evolution of cooperation is a paradox from an evolutionary point of view. Indeed, all individuals in the living world should be interested only in their own interests. Helping another individual is therefore a waste of time and resources. How can we explain that some individuals act in a cooperative way? This thesis focuses on the mechanism of reciprocity named partner choice that allows the evolution of cooperative behavior. However, the constraints necessary for the evolution of this mechanism are strong and rarely respected. This thesis aims at extending the knowledge of these constraints through simulations in complex and realistic environments. The first contribution of this thesis extends the pre-existing partner choice models by adding the constraint of resource availability in the environment. In this case, it is no longer only the population density, but also the wealth of the environment that influences partner choice. In our second contribution, we extend the results obtained by the aspatial models. We study the impact of spatial environments on the dynamics of cooperation with partner choice. Partner choice requires individuals to be able to learn from rare events. In our third contribution, we compare the reward sparsity tolerance of a reinforcement learning algorithm and an evolutionary strategy algorithm. Our work shows the immense difficulty that reinforcement learning algorithms have in developing partner choice, unlike evolutionary strategies.
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Submitted on : Monday, May 30, 2022 - 2:55:48 PM
Last modification on : Wednesday, June 1, 2022 - 9:15:03 AM

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  • HAL Id : tel-03681698, version 1

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Paul Ecoffet. Evolution of cooperation with partner choice in collective adaptive systems. Artificial Intelligence [cs.AI]. Sorbonne Université, 2021. English. ⟨NNT : 2021SORUS091⟩. ⟨tel-03681698⟩

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