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Showing 1–6 of 6 results
Advanced filters: Author: Karl Tuyls Clear advanced filters

  • Multiplayer games can be used as testbeds for the development of learning algorithms for artificial intelligence. Omidshafiei et al. show how to characterize and compare such games using a graph-based approach, generating new games that could potentially be interesting for training in a curriculum.

    • Shayegan Omidshafiei
    • Karl Tuyls
    • Rémi Munos
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-17

  • Koster et al introduce a deep reinforcement learning (RL) mechanism designed to manage common-pool resources successfully encourages sustainable cooperation among human participants by dynamically adjusting resource allocations based on the current state of the resource pool. The RL-derived policy outperforms traditional allocation methods by balancing generosity when resources are abundant and applying temporary sanctions to discourage free-riding, ultimately maximizing social welfare and fairness.

    • Raphael Koster
    • Miruna Pîslar
    • Christopher Summerfield
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-13

  • In modern football games, data-driven analysis serves as a key driver in determining tactics. Wang, Veličković, Hennes et al. develop a geometric deep learning algorithm, named TacticAI, to solve high-dimensional learning tasks over corner kicks and suggest tactics favoured over existing ones 90% of the time.

    • Zhe Wang
    • Petar Veličković
    • Karl Tuyls
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-13