Summary of Monte Carlo Tree Search Satellite Scheduling Under Cloud Cover Uncertainty, by Justin Norman and Francois Rivest
Monte Carlo Tree Search Satellite Scheduling Under Cloud Cover Uncertainty
by Justin Norman, Francois Rivest
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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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 paper addresses the multi-satellite collection scheduling problem (m-SatCSP), which aims to optimize task scheduling over a constellation of satellites under uncertain conditions such as cloud cover. The authors leverage Monte Carlo Tree Search (MCTS), a stochastic search algorithm, and explore two versions of MCTS to schedule satellites effectively. Hyperparameter tuning is conducted to optimize the algorithm’s performance. Experimental results demonstrate the effectiveness of the MCTS approach, outperforming existing methods in both solution quality and efficiency. Comparative analysis against other scheduling algorithms showcases competitive performance, positioning MCTS as a promising solution for satellite task scheduling in dynamic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with satellites that need to collect data when there’s cloud cover or uncertainty. They use a special kind of computer search called Monte Carlo Tree Search (MCTS) to schedule the satellites effectively. Two versions of this search were tried and it was shown to be better than other methods in both how well it did and how quickly it did it. This is important for using satellites to collect data when there’s uncertainty. |
Keywords
» Artificial intelligence » Hyperparameter
