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.
On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters
2021-01-01
Smyrnakis , M , Qu , H , Bauso , D & Veres , S 2021 , On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters . in A P Rocha , L Steels & J van den Herik (eds) , Agents and Artificial Intelligence - 12th International Conference, ICAART 2020, Revised Selected Papers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12613 LNAI , Springer Science and Business Media Deutschland GmbH , pp. 73-105 , 12th International Conference on Agents and Artificial Intelligence, ICAART 2020 , Valletta , Malta , 22/02/2020 . https://doi.org/10.1007/978-3-030-71158-0_4
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
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