Summary of Inverse Attention Agent For Multi-agent System, by Qian Long et al.
Inverse Attention Agent for Multi-Agent System
by Qian Long, Ruoyan Li, Minglu Zhao, Tao Gao, Demetri Terzopoulos
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
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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 a novel approach for multi-agent systems, focusing on enabling agents to adapt dynamically to diverse environments where opponents and teammates may change continuously. The authors introduce Inverse Attention Agents that incorporate concepts from the Theory of Mind (ToM) through an attention mechanism trained end-to-end. These agents’ attention models explicitly represent attention to different goals, allowing them to adjust their actions based on observations and prior actions. A proposed inverse attention network infers the ToM of other agents, refining attention weights for improved performance. The authors conduct experiments in a continuous environment, demonstrating that the inverse attention network successfully infers attention and improves agent performance. Human experiments show that inverse attention agents excel in cooperation with humans and emulate human behaviors better than baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem where computer programs (called “agents”) have trouble working well when they encounter new people or situations. The authors suggest a new way to train these agents, using ideas from how humans think about each other’s thoughts (Theory of Mind). They use this idea to create an “attention mechanism” that helps the agents decide what actions to take based on what they see and have done before. A special kind of network is proposed to understand what other agents are thinking and adjust its own attention accordingly. The authors tested their ideas in computer simulations and found that it worked well. They also showed that these new agents do a better job working with humans than older methods. |
Keywords
» Artificial intelligence » Attention
