Summary of Slope: Search with Learned Optimal Pruning-based Expansion, by Davor Bokan et al.
SLOPE: Search with Learned Optimal Pruning-based Expansion
by Davor Bokan, Zlatan Ajanovic, Bakir Lacevic
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 Search with Learned Optimal Pruning-based Expansion (SLOPE) algorithm addresses the limitations of traditional heuristic search methods for motion planning and pathfinding problems. SLOPE learns the distance of a node from a possible optimal path, allowing it to prune unfavored nodes based on this distance, reducing memory and computational costs. This approach is orthogonal to estimating cost-to-go heuristics and offers a complementary strategy for improving search efficiency. The authors demonstrate the effectiveness of SLOPE as a standalone search method and in conjunction with learned heuristic functions, achieving comparable or better node expansion metrics while reducing the number of child nodes in the open list. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SLOPE is a new way to do motion planning on computers. It helps find the shortest path between two points by learning how far away each point is from the best possible route. This makes it faster and uses less memory than other methods. SLOPE is useful for robots or self-driving cars that need to make decisions quickly. |
Keywords
» Artificial intelligence » Pruning
