# Modeling and Predicting Human Actions in Soccer Using Tensor-SOM
## 8 Conclusions and Future Work  
Tensor-SOM effectively models team decision-making but struggles with sudden speed changes and y-direction movements. The results presented in Figures 9-16 demonstrate that the system's output (shown in blue) is largely consistent with the training data (shown in orange). However, the system's output tends to be slightly lower on average. This discrepancy may be attributed to the simultaneous training of multiple agents, which could have led to the cancellation of individual agent characteristics. Further analysis is required to determine the extent to which this effect influences the overall performance of the model. Additionally, the results indicate that the velocity component in the x-direction (\(V_{x}\)) is more accurately modeled than the velocity component in the y-direction (\(V_{y}\)). This suggests that movement in the x-direction plays a more significant role in offensive and defensive strategies within the given context. The difference in modelability between these two velocity components highlights the importance of considering domain-specific movement characteristics when designing predictive models for robotic decision-making. To improve the system's accuracy, future work should explore methods to reduce the observed bias in the output and align it more closely with the values observed in the training data. One potential approach involves adjusting the learning process to better capture the unique characteristics of individual agents while maintaining generalization capability. Fine-tuning the training parameters or incorporating additional contextual information may further enhance the model's predictive performance. In this study, we developed an action acquisition system based on the Tensor Self-Organizing Map, which enables learning of input-output relationships through unsupervised training. Our findings confirm that Tensor-SOM successfully captures the underlying structure of the data, as demonstrated by the convergence of the learning process. These results suggest that Tensor-SOM is a viable approach for modeling complex human movement patterns and can be effectively applied to decision-making in autonomous robotic systems. Moving forward, we aim to refine the proposed system by incorporating additional environmental factors and improving its adaptability to dynamic scenarios. Furthermore, extending this framework to real-time decision-making applications in robotics could pave the way for more sophisticated and responsive autonomous systems capable of interacting seamlessly with human teams. Future work will also explore several important directions to enhance the practical applicability of the proposed system. First, we plan to incorporate and evaluate real-time learning capabilities, enabling agents to adapt their behavior dynamically in response to changing environments. Second, the system will be tested on larger-scale datasets to assess its scalability and generalization performance under more diverse and complex scenarios. Lastly, we aim to investigate the integration of Tensor-SOM with other learning frameworks, such as reinforcement learning, to examine potential synergies between unsupervised and reward-based learning paradigms. These extensions are expected to further improve the adaptability, robustness, and real-world applicability of the proposed approach. Conceptualization, K.I. and M.T. ; methodology, M.T. ; software, Y.T. ; validation, M.T. ; writing--original draft preparation, M.T. ; writing--review and editing, K.I. and Y.T. ; visualization, M.T. ; supervision, K.I. ; project administration, M.T. ; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by JSPS KAKENHI Grant Number 24K20876. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in this study are included in the article.