Summary of Relevance Score: a Landmark-like Heuristic For Planning, by Oliver Kim and Mohan Sridharan
Relevance Score: A Landmark-Like Heuristic for Planning
by Oliver Kim, Mohan Sridharan
First submitted to arxiv on: 12 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 A novel “relevance score” is proposed to identify facts or actions that appear in most but not all plans to achieve a given goal. This extension to landmark-based heuristics guides the search for a plan by computing relevance scores, which are then used as a heuristic. The approach is experimentally compared with state-of-the-art landmark-based planning methods using benchmark problems. Results show that while the original landmark-based method excels on well-defined landmarks, our proposed approach improves performance on problems lacking non-trivial landmarks. Techniques such as landmark-based heuristics and relevance scores are explored in this paper, demonstrating potential applications in planning domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores new ways to help computers find solutions to complex problems. The idea is to identify important “landmarks” that appear in many possible solutions. A new method called the “relevance score” helps pinpoint these landmarks, which can guide the search for a solution. The approach is tested on different problem sets and shows promise in finding better solutions when traditional methods fail. This paper contributes to understanding how computers can better tackle complex planning problems. |
