Summary of Separate Generation and Evaluation For Parallel Greedy Best-first Search, by Takumi Shimoda and Alex Fukunaga
Separate Generation and Evaluation for Parallel Greedy Best-First Search
by Takumi Shimoda, Alex Fukunaga
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Data Structures and Algorithms (cs.DS)
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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 an improvement to parallel Greedy Best First Search (GBFS) algorithms, which can explore states that would not be explored by sequential GBFS. This is achieved by decoupling state generation and evaluation, allowing for a significant increase in state evaluation rate and better search performance. The proposed method constrains the search to explore only the Bench Transition System (BTS), which is the set of states that can be expanded by GBFS under some tie-breaking policy. This approach improves upon previous work that enforced this constraint at a high cost, allowing for more efficient exploration of the state space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an innovative way to improve parallel search algorithms in AI. By separating the process of generating new states and evaluating them, the algorithm can explore the problem space more efficiently. This results in better performance when searching for solutions. The idea is to focus on exploring the most promising areas of the state space, rather than wasting time on less useful paths. |
