Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories

TL;DR

This paper introduces a deep decision tree model for discriminative dictionary learning from multi-agent trajectories, enhancing interpretability and analysis of adversarial group behaviors, especially in sports like soccer.

Contribution

It presents a novel hierarchical decision tree architecture that learns interpretable group interaction dictionaries from adversarial multi-agent trajectory data.

Findings

Effective in modeling soccer team strategies

Provides interpretable multi-agent interaction representations

Applicable to various adversarial multi-agent domains

Abstract

With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments. In this paper, we propose a deep decision tree architecture for discriminative dictionary learning from adversarial multi-agent trajectories. We first build up a hierarchy for the tree structure by adding each layer and performing feature weight based clustering in the forward pass. We then fine tune the player role weights using back propagation. The hierarchical architecture ensures the interpretability and the integrity of the group representation. The resulting architecture is a decision tree, with leaf-nodes capturing a dictionary of multi-agent group interactions. Due to the ample volume of data available, we focus on soccer tracking data, although our approach can be used in any adversarial multi-agent domain. We present applications of proposed method for simulating soccer games as well as evaluating and quantifying team strategies.