3 papers
cs.LG2026
Strategic Multi-Agent Learning for Interpretable Action Valuation of All Players in Football
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Valuing player actions in football, a complex multi-agent system, requires accounting for strategic interactions among 22 players, including off-ball movements and defensive positi…
cs.AI2025
Expandable Decision-Making States for Multi-Agent Deep Reinforcement Learning in Soccer Tactical Analysis
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Invasion team sports such as soccer produce a high-dimensional, strongly coupled state space as many players continuously interact on a shared field, challenging quantitative tacti…
cs.AI2025
Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics
Kenjiro Ide, Taiga Someya, Kohei Kawaguchi +1
Understanding football tactics is crucial for managers and analysts. Previous research has proposed models based on spatial and kinematic equations, but these are computationally e…