Summary of Resource Constrained Pathfinding with Enhanced Bidirectional A* Search, by Saman Ahmadi et al.
Resource Constrained Pathfinding with Enhanced Bidirectional AuthorLineProcess.function Search
by Saman Ahmadi, Andrea Raith, Guido Tack, Mahdi Jalili
First submitted to arxiv on: 18 Dec 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 novel constrained search framework leverages efficient pruning strategies within the bidirectional A* search paradigm to accelerate and effectively solve large-scale Resource Constrained Shortest Path (RCSP) problems. By exploiting heuristic-guided search, this approach outperforms state-of-the-art methods by achieving speed-ups of over two orders of magnitude. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a classic problem called the Resource Constrained Shortest Path. Imagine you want to find the best route between two places in a big network, but you can only use certain resources along the way. The researchers created a new way to search for this path that is much faster than before. It’s like having a superpower GPS system! This helps us solve huge problems more quickly. |
Keywords
» Artificial intelligence » Pruning
