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On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

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Agents and Artificial Intelligence (ICAART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12613))

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Abstract

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.

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Notes

  1. 1.

    The strategies are probability distributions. Thus, when a dynamical model is used to propagate them, new estimates are not necessary to lay in the probability distributions space. For that reason, the intentions of players to choose an action, namely propensities, which are not bounded to probability distribution spaces, are used [56].

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Author information

Authors and Affiliations

  1. Science and Technology Facilities Council, Daresboury, UK

    Michalis Smyrnakis

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Hongyang Qu & Sandor Veres

  3. Jan C. Willems Center for Systems and Control ENTEG, Faculty of Science and Engineering, University of Groningen Nijenborgh, Groningen, The Netherlands

    Dario Bauso

  4. Dipartimento di Ingegneria, Università di Palermo, Viale delle Scienze, Palermo, Italy

    Dario Bauso

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  1. Michalis Smyrnakis
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  2. Hongyang Qu
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  4. Sandor Veres
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Correspondence to Michalis Smyrnakis .

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Editors and Affiliations

  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

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Smyrnakis, M., Qu, H., Bauso, D., Veres, S. (2021). On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2020. Lecture Notes in Computer Science(), vol 12613. Springer, Cham. https://doi.org/10.1007/978-3-030-71158-0_4

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