Summary of Where Common Knowledge Cannot Be Formed, Common Belief Can — Planning with Multi-agent Belief Using Group Justified Perspectives, by Guang Hu et al.
Where Common Knowledge Cannot Be Formed, Common Belief Can – Planning with Multi-Agent Belief Using Group Justified Perspectives
by Guang Hu, Tim Miller, Nir Lipovetzky
First submitted to arxiv on: 10 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 Epistemic planning in AI focuses on changing knowledge and beliefs. In multi-agent settings, agents need to reason about their own knowledge and the beliefs of others, including nested beliefs. Planning with Perspectives (PWP) is a contemporary method that addresses challenges posed by exponential growth in nested depth. The JP model defines justified belief as evidence-based and time-stable. This paper extends the JP model to handle group belief, introducing the Group Justified Perspective (GJP) model. We demonstrate GJP’s efficiency and expressiveness using adapted benchmarks from well-known planning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about how artificial intelligence can make better decisions by understanding how people think and believe. When many agents work together, they need to understand each other’s thoughts and beliefs. This paper introduces a new way for AI to handle these complex belief systems, called the Group Justified Perspective (GJP) model. We test GJP using problems adapted from popular benchmarks. |
