Summary of Anytime Multi-agent Path Finding with An Adaptive Delay-based Heuristic, by Thomy Phan et al.
Anytime Multi-Agent Path Finding with an Adaptive Delay-Based Heuristic
by Thomy Phan, Benran Zhang, Shao-Hung Chan, Sven Koenig
First submitted to arxiv on: 6 Aug 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 proposes a new approach to multi-agent path finding (MAPF), specifically an adaptive single-destroy-heuristic variant of Large Neighborhood Search (LNS) called Adaptive Delay-based Destroy-and-Repair Enhanced with Success-based Self-Learning (ADDRESS). This method applies Thompson Sampling to the top-K set of delayed agents, selecting a seed agent for neighborhood generation. The authors evaluate ADDRESS on multiple maps from the MAPF benchmark set and demonstrate cost improvements of at least 50% in large-scale scenarios with up to a thousand agents, outperforming original MAPF-LNS and other state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAPF is a way to help lots of robots or cars find the best paths. It’s like finding the shortest route for a delivery service. The current best method is called MAPF-LNS, which takes a good starting solution and makes it better by changing some of the routes. But this method can get stuck if it chooses the wrong way to improve the solution. This paper proposes a new method that uses an algorithm called Thompson Sampling to choose the best route to change. They tested this method on lots of scenarios with up to 1000 robots or cars and found that it’s at least 50% better than other methods. |
