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Learning in Networked Interactions: A Replicator Dynamics Approach

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Artificial Life and Intelligent Agents (ALIA 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 519))

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Abstract

Many real-world scenarios can be modelled as multi-agent systems, where multiple autonomous decision makers interact in a single environment. The complex and dynamic nature of such interactions prevents hand-crafting solutions for all possible scenarios, hence learning is crucial. Studying the dynamics of multi-agent learning is imperative in selecting and tuning the right learning algorithm for the task at hand. So far, analysis of these dynamics has been mainly limited to normal form games, or unstructured populations. However, many multi-agent systems are highly structured, complex networks, with agents only interacting locally. Here, we study the dynamics of such networked interactions, using the well-known replicator dynamics of evolutionary game theory as a model for learning. Different learning algorithms are modelled by altering the replicator equations slightly. In particular, we investigate lenience as an enabler for cooperation. Moreover, we show how well-connected, stubborn agents can influence the learning outcome. Finally, we investigate the impact of structural network properties on the learning outcome, as well as the influence of mutation driven by exploration.

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Notes

  1. 1.

    We describe stateless Q-learning, as this version is suitable for the work presented in this paper.

  2. 2.

    Note that we exclude these fixed nodes from the results presented here, however a similar trend can be observed it they are included.

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Acknowledgements

We thank the anonymous reviewers as well as the audience at the Artificial Life and Intelligent Agents symposium for their helpful comments and suggestions.

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Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, UK

    Daan Bloembergen, Ipek Caliskanelli & Karl Tuyls

  2. Department of Knowledge Engineering, Maastricht University, Maastricht, The Netherlands

    Daan Bloembergen

Authors
  1. Daan Bloembergen
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  2. Ipek Caliskanelli
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  3. Karl Tuyls
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Correspondence to Daan Bloembergen .

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Editors and Affiliations

  1. Bangor University, Bangor, United Kingdom

    Christopher J. Headleand

  2. Bangor University, Bangor, United Kingdom

    William J. Teahan

  3. Bangor University, Bangor, United Kingdom

    Llyr Ap Cenydd

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Bloembergen, D., Caliskanelli, I., Tuyls, K. (2015). Learning in Networked Interactions: A Replicator Dynamics Approach. In: Headleand, C., Teahan, W., Ap Cenydd, L. (eds) Artificial Life and Intelligent Agents. ALIA 2014. Communications in Computer and Information Science, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-319-18084-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-18084-7_4

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