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Gregory Everett

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2

AAAI Conference 2025 Conference Paper

Optimising Spatial Teamwork Under Uncertainty

  • Gregory Everett
  • Ryan J. Beal
  • Tim Matthews
  • Timothy J. Norman
  • Sarvapali D. Ramchurn

We introduce a novel method for assessing agent teamwork based on their spatial coordination. Our approach models the influence of spatial proximity on team formation and sustained spatial dominance over adversaries using a Multi-agent Markov Decision Process. We develop an algorithm to derive efficient teamwork strategies by combining Monte Carlo Tree Search and linear programming. When applied to team defence in football (soccer) using real-world data, our approach reduces opponent threat by 21%, outperforming optimised individual behaviour by 6%. Additionally, our model enhances the predictive accuracy of future attack locations and provides deeper insights compared to existing teamwork models that do not explicitly consider the spatial dynamics of teamwork.

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AAMAS Conference 2023 Conference Paper

Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations

  • Gregory Everett
  • Ryan J. Beal
  • Tim Matthews
  • Joseph Early
  • Timothy J. Norman
  • Sarvapali D. Ramchurn

Understanding agent behaviour in Multi-Agent Systems (MAS) is an important problem in domains such as autonomous driving, disaster response, and sports analytics. Existing MAS problems typically use uniform timesteps with observations for all agents. In this work, we analyse the problem of agent location imputation, specifically posed in environments with non-uniform timesteps and limited agent observability (∼95% missing values). Our approach uses Long Short-Term Memory and Graph Neural Network components to learn temporal and inter-agent patterns to predict the location of all agents at every timestep. We apply this to the domain of football (soccer) by imputing the location of all players in a game from sparse event data (e. g. , shots and passes). Our model estimates player locations to within ∼6. 9m; a ∼62% reduction in error from the best performing baseline. This approach facilitates downstream analysis tasks such as player physical metrics, player coverage, and team pitch control. Existing solutions to these tasks often require optical tracking data, which is expensive to obtain and only available to elite clubs. By imputing player locations from easy to obtain event data, we increase the accessibility of downstream tasks.

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