Summary of Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents Via Efficient Guidance-exploration, by Zijian Wang et al.
Walk Wisely on Graph: Knowledge Graph Reasoning with Dual Agents via Efficient Guidance-Exploration
by Zijian Wang, Bin Wang, Haifeng Jing, Huayu Li, Hongbo Dou
First submitted to arxiv on: 3 Aug 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 This paper proposes a multi-hop reasoning model called FULORA for knowledge graphs, which addresses two primary shortcomings of previous approaches. FULORA uses hierarchical reinforcement learning (HRL) with dual agents to tackle challenges like sparse rewards and lengthy reasoning paths in datasets like sparse knowledge graphs. The high-level agent provides stage-wise hints for the low-level agent, optimizing a value function that balances return maximization and efficient guidance integration. Experimental results on three real-world knowledge graph datasets show that FULORA outperforms RL-based baselines, especially in long-distance reasoning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand big data called FULORA. It’s like having two helpers working together to find answers. The first helper looks at the main ideas and gives hints to the second helper, who then searches through the details. This helps them make better decisions and find answers more easily. The scientists tested this idea on real-world datasets and found that it works really well, especially when they need to look very far ahead. |
Keywords
» Artificial intelligence » Knowledge graph » Reinforcement learning
