Summary of A Factor Graph Model Of Trust For a Collaborative Multi-agent System, by Behzad Akbari et al.
A Factor Graph Model of Trust for a Collaborative Multi-Agent System
by Behzad Akbari, Mingfeng Yuan, Hao Wang, Haibin Zhu, Jinjun Shan
First submitted to arxiv on: 10 Feb 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 The proposed graphical approach in Multi-Agent Systems (MAS) enables agents to rely on resources and services from others by modeling interdependent behaviors and trustworthiness using factor graphs. This includes representing robot behavior as a trajectory of actions, accounting for smoothness, obstacle avoidance, and trust-related factors. The method evaluates trust through decentralized considerations of proximity safety, consistency, and cooperation. A network of interacting factor graphs employs Bayesian inference to dynamically assess trust-based decisions with informed consent. Simulations and empirical tests with autonomous robots validate the effectiveness of this approach in navigating unsignalized intersections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about how agents can work together safely and effectively by trusting each other’s actions and intentions. The researchers developed a new way to model these interactions using “factor graphs,” which are like diagrams that show how different things are connected. This approach takes into account things like how close the robots are to each other and whether they’re moving in a safe way. The method is tested on real-world scenarios with autonomous robots, showing that it can help them navigate complex situations like unsignalized intersections. |
Keywords
» Artificial intelligence » Bayesian inference
