Summary of An Integrated Framework For Team Formation and Winner Prediction in the First Robotics Competition: Model, Algorithm, and Analysis, by Federico Galbiati et al.
An Integrated Framework for Team Formation and Winner Prediction in the FIRST Robotics Competition: Model, Algorithm, and Analysis
by Federico Galbiati, Ranier X. Gran, Brendan D. Jacques, Sullivan J. Mulhern, Chun-Kit Ngan
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research develops an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on prior data about competitors’ skills. The method allows for constraints such as teams with set members, unlike previous studies that focus on individual member statistics or specific team structures. To address the unique challenges of the FIRST Robotics Competition, where team members and skills change year-over-year, an alliance optimization algorithm is developed to optimize team formation, and a deep neural network model is trained to predict the winning team using highly post-processed real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create a better way to build teams for competitions like the FIRST Robotics Competition. Right now, there isn’t a good method that takes into account things like changing team members and skills, so this study tries to fill that gap. The researchers developed two main tools: one that can predict individual member performance based on overall team performance, and another that optimizes team formation for competitive success. They used real-world data from past competitions and were able to accurately predict the winning team 84% of the time. |
Keywords
* Artificial intelligence * Neural network * Optimization
