Jadson Nobre
Papers
2
Total Citations
10
H-Index
2
About
Jadson Nobre is a researcher advancing the field of multi-agent systems and robotic coordination, with a primary focus on strategic planning for robotic soccer. His key research areas include setplay generation, learning from demonstration (LfD), and multi-robot coordination in dynamic environments. Nobre’s major contributions center on the development and enhancement of the Strategy Planner (SPlanner) and the FCPortugal Setplays Framework (FSF), tools that enable robotic soccer teams to design and execute sophisticated coordinated plans. His work on generating datasets for learning setplays from demonstration, published in 2021, has been cited 5 times and provides a foundational approach for enabling robots to acquire complex team strategies without manual programming. Additionally, his 2020 paper on enhancing SPlanner to support better defense and pass strategies, also with 5 citations, demonstrates his commitment to improving real-time tactical decision-making in multi-agent systems. These contributions are particularly notable for bridging the gap between human-designed strategies and autonomous learning, making his work valuable for researchers in robotics, artificial intelligence, and multi-agent coordination.
Research Focus
Key Achievements
Top Papers
- 1Generating a dataset for learning setplays from demonstration5 citations · 2021
- 2