# Modeling and Predicting Human Actions in Soccer Using Tensor-SOM
MAS are composed of autonomous agents that interact with each other and with the environment to achieve common goals, typically based on local observations and real-time decision-making. Although centralized control systems are often used to coordinate such agents due to their simplicity and effectiveness, they may lack scalability and robustness in dynamic environments. Enhancing the autonomy of individual agents is essential for achieving more flexible and decentralized coordination [5]. However, developing fully autonomous robots remains a challenging task, requiring capabilities such as motion control, environmental perception, and self-localization [6; 7]. Despite advancements in learning-based algorithms and soft computing techniques, real-world deployment of Human-Robot Interaction (HRI) and Multi-Agent Systems (MAS) continues to face significant challenges. In HRI, issues such as reproducibility, generalizability across different contexts, and ethical considerations--including trust, privacy, and user acceptance--remain critical concerns [8; 9]. Similarly, MAS implementations often grapple with problems like communication delays, partial observability, and scalability, which can hinder effective coordination and decision-making among agents [10]. Addressing these challenges is essential for the successful integration of robotic systems into dynamic and unstructured real-world environments. The domain of soccer, especially in the context of human actions, provides a rich and dynamic testbed for developing and evaluating autonomous decision-making systems. Human soccer behavior is inherently multi-agent, strategic, and responsive to fast-changing conditions--qualities that closely mirror many real-world scenarios where autonomous robots are expected to operate, such as search and rescue, autonomous driving, and logistics. By modeling robot behavior based on human-like soccer interactions, we can simulate and study complex coordination, competition, and adaptation under uncertainty. This not only challenges the agents in a controlled, replicable environment like RoboCup but also yields transferable insights that can improve real-world robotic applications requiring teamwork, perception, and decision-making under constraints. The RoboCup Soccer competition serves as a representative MAS testbed, providing a dynamic and interactive environment for testing cooperative decision-making strategies. In particular, the RoboCup Middle Size League (MSL) features soccer-playing robots that must interpret complex sensor data, recognize their state and surroundings, and determine appropriate actions during a match. Recent approaches have employed learning-based methods, including reinforcement learning and neural networks, to address these challenges. This study focuses on the use of Self-Organizing Maps (SOM), an unsupervised neural network model, to develop a behavior decision-making system for MSL soccer robots. SOM is capable of dimensionality reduction and feature extraction, making it suitable for interpreting high-dimensional sensor data and recognizing relevant environmental patterns [11]. Furthermore, to analyze team dynamics and cooperative behavior, we address relational data--large-scale data characterized by interdependent elements. For this purpose, we employ the Tensor Self-Organizing Map (Tensor-SOM), an extension of SOM that enables interpolation and modeling of complex input-output relationships in multidimensional spaces. Tensor-SOM allows for intuitive visualization of team behaviors and facilitates understanding of coordinated decision-making processes [12; 13]. However, one important consideration in robot soccer, which involves dynamic physical interactions and motion constraints, is whether approximation methods could serve as a viable alternative for behavior decision-making.