Summary of On the Completeness Of Conflict-based Search: Temporally-relative Duplicate Pruning, by Thayne T Walker and Nathan R Sturtevant
On the Completeness of Conflict-Based Search: Temporally-Relative Duplicate Pruning
by Thayne T Walker, Nathan R Sturtevant
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 paper proposes Temporally-Relative Duplicate Pruning (TRDP), a technique for duplicate detection and removal in Multi-Agent Pathfinding (MAPF) problems. MAPF is an incomplete algorithm that can run forever on unsolvable instances, but TRDP closes this loophole by detecting and avoiding duplicate states. This approach ensures termination without significantly impacting runtime in most cases. Additionally, TRDP is shown to increase performance in certain situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem with a computer program called Multi-Agent Pathfinding (MAPF). MAPF can get stuck on some problems and never finish. The authors created a new way to detect when the same solution has been found multiple times, called Temporally-Relative Duplicate Pruning (TRDP). This helps MAPF stop running when it’s not making progress, which makes it faster overall. |
Keywords
» Artificial intelligence » Pruning
