Multi-model adaptive learning for robots under uncertainty

Michalis Smyrnakis, Hongyang Qu, Dario Bauso, Sandor Veres

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)


This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. 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. In contrast, to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters of the algorithm for a specific problem a priori. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.

Original languageEnglish
Title of host publicationICAART 2020 - Proceedings of the 12th International Conference on Agents and Artificial Intelligence
EditorsAna Rocha, Luc Steels, Jaap van den Herik
Number of pages12
ISBN (Electronic)9789897583957
Publication statusPublished - 2020
Event12th International Conference on Agents and Artificial Intelligence, ICAART 2020 - Valletta, Malta
Duration: 22-Feb-202024-Feb-2020


Conference12th International Conference on Agents and Artificial Intelligence, ICAART 2020


  • Fictitious Play
  • Multi-model Adaptive Learning
  • Robotic Teams
  • Task Allocation

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