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Patrick Lucey

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4 papers

2 author rows

AAAI Conference 2025 Conference Paper

Event2Tracking: Reconstructing Multi-Agent Soccer Trajectories Using Long-Term Multimodal Context

Harry Hughes
Michael Horton
Xinyu Wei
Harshala Gammulle
Clinton Fookes
Sridha Sridharan
Patrick Lucey

Soccer is a rich testbed for studying multi-agent adversarial systems. In this work we focus on the task of reconstructing the noisy trajectories of soccer agents (players and the ball). Previous works that model the behaviours of agents in soccer are limited in two respects: (i) they only focus on short-term context windows (less than or equal to 10 seconds) which are not suitable for reconstructing trajectories impacted by long-term noise, and (ii) they exclusively rely on trajectory context, and do not leverage soccer's auxiliary data streams that can provide additional context. Our Event2Tracking model addresses these limitations. First, our architecture models soccer's long-term structure by processing long-term trajectories (60 seconds in duration). Secondly, our architecture is multimodal. Specifically, it fuses soccer tracking data with event data (which specifies the high-level semantic events that transpire in a game), providing rich context that cannot strictly be inferred from the raw trajectories. We evaluate our method empirically using a reconstruction loss metric. Compared to state-of-the-art approaches, our method substantially improves the accuracy of the ball's and players' reconstructed trajectories.

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ICML Conference 2017 Conference Paper

Coordinated Multi-Agent Imitation Learning

Hoang Minh Le 0002
Yisong Yue
Peter Carr 0001
Patrick Lucey

We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.

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NeurIPS Conference 2015 Conference Paper

Softstar: Heuristic-Guided Probabilistic Inference

Mathew Monfort
Brenden Lake
Brian Ziebart
Patrick Lucey
Josh Tenenbaum

Recent machine learning methods for sequential behavior prediction estimate the motives of behavior rather than the behavior itself. This higher-level abstraction improves generalization in different prediction settings, but computing predictions often becomes intractable in large decision spaces. We propose the Softstar algorithm, a softened heuristic-guided search technique for the maximum entropy inverse optimal control model of sequential behavior. This approach supports probabilistic search with bounded approximation error at a significantly reduced computational cost when compared to sampling based methods. We present the algorithm, analyze approximation guarantees, and compare performance with simulation-based inference on two distinct complex decision tasks.

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AAAI Conference 2012 Conference Paper

Characterizing Multi-Agent Team Behavior from Partial Team Tracings: Evidence from the English Premier League

Patrick Lucey
Alina Bialkowski
Peter Carr
Eric Foote
Iain Matthews

Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i. e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player’s plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into “discretized” plays (i. e. plans), making it difficult to obtain a library that characterizes a team’s behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time. Using these representations we can characterize team behavior from entropy maps, which give a measure of predictability of team behaviors across the field. We show the efficacy and applicability of our method on the 2010-2011 English Premier League soccer data.

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