Summary of Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization, by Saeid Amiri et al.
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization
by Saeid Amiri, Parisa Zehtabi, Danial Dervovic, Michael Cashmore
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper tackles facility location problems in industries, focusing on minimizing sales loss when closing retail stores. The authors aim to efficiently estimate accurate sales data, which is typically time-consuming and costly. To address this challenge, they employ Monte Carlo Tree Search (MCTS) augmented by a surrogate model that computes evaluations quickly. By leveraging MCTS with a fast surrogate function, the proposed approach can generate solutions faster while maintaining consistent quality compared to using only MCTS without the surrogate. The paper’s findings demonstrate the potential of combining MCTS and surrogates for solving facility location problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to decide which stores to close in a shopping mall to make more money. It would be helpful if you could predict how much each store sells, but that can take a lot of time and money. This paper shows a way to solve this problem by using a special computer program called Monte Carlo Tree Search (MCTS) with another tool that helps it work faster. The results show that this combination can help find the best solution quickly and accurately. |
