# Modeling and Predicting Human Actions in Soccer Using Tensor-SOM
## 2 Development of Algorithms for Agent Behavior Determination  
Previous studies have analyzed the determinants and methodologies for agent behavior decision-making within multi-agent systems (MAS). It has been suggested that, for both humans and robotic agents, the relationships with other agents at a given moment serve as crucial factors influencing behavioral decisions [16].  
Moreover, various algorithms for behavior determination have been investigated, with particular attention paid to the application of Tensor Self-Organizing Maps (Tensor-SOM). This approach has demonstrated potential in mapping sequences of behaviors in a structured and interpretable manner [17].  
A promising method for facilitating mutual learning among agents involves the use of tensor data. Tensor data, often referred to as relational data [18], are widely utilized in big data analytics and are especially effective in handling data characterized by high diversity and complexity. By employing tensor representations, it becomes possible for individual agents to influence each other's learning processes, even under identical environmental conditions. Such mutual influence can foster the emergence of novel behavioral strategies. The greater the number and diversity of agents and scenarios involved in training, the more extensive and diverse the learned behavior repertoire becomes. Consequently, an algorithm with robust interpolation capabilities is desirable.  
This property is particularly advantageous not only for robotic agents but also when integrating human soccer behavior data, where an increased volume and density of data contribute to more effective learning outcomes. In this study, we developed a behavior determination algorithm based on the Self-Organizing Map (SOM) framework, aiming to exploit the advantages of tensor-based learning in MAS environments.  
In the context of RoboCup, numerous studies have focused on the development and refinement of agent behavior models to enable effective team coordination and decision-making in dynamic, adversarial environments. For example, early RoboCup teams widely employed rule-based systems and finite state machines to encode predefined strategies and reactive behaviors [19]. Although these methods offer robustness in controlled situations, they exhibit limited adaptability to unforeseen scenarios.  
In recent years, machine learning approaches--particularly those based on reinforcement learning and neural network architectures--have been adopted to enhance agents' adaptability and decision-making abilities [20; 21]. These techniques allow agents to acquire optimal actions through interactions with both their environment and teammates, thereby reducing reliance on hand-crafted rules.  
Furthermore, several studies have incorporated models that account for the spatio-temporal dynamics of the game--such as opponent positioning and ball trajectories--as core inputs for behavior generation [22]. These models have significantly improved agents' strategic awareness, resulting in more context-sensitive decision-making.  
Despite these advancements, accurately capturing the relational complexity among agents under varying game contexts remains a significant challenge. This limitation underscores the potential of tensor-based approaches, such as Tensor-SOM, which are capable of modeling and leveraging multidimensional relationships among agents, actions, and temporal sequences in a scalable and interpretable manner.