Summary of Autonomous Agents For Collaborative Task Under Information Asymmetry, by Wei Liu et al.
Autonomous Agents for Collaborative Task under Information Asymmetry
by Wei Liu, Chenxi Wang, Yifei Wang, Zihao Xie, Rennai Qiu, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Chen Qian
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA); Social and Information Networks (cs.SI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces Informative Multi-Agent Systems (iAgents), a new paradigm for Large Language Model Multi-Agent Systems (LLM-MAS) that addresses the challenge of information asymmetry in multi-person tasks. iAgents mirror human social networks, allowing agents to proactively exchange necessary information for task resolution. The system employs InfoNav, a novel agent reasoning mechanism, and mixed memory to organize human information. This enables agents to communicate effectively and complete tasks under information asymmetry. The paper also proposes InformativeBench, the first benchmark for evaluating LLM agents’ task-solving ability in this context. Experimental results show that iAgents can collaborate with humans, autonomously communicate, and retrieve information from large datasets to complete tasks efficiently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way for computers (agents) to work together to solve problems. Right now, these agents are only good at working individually, but they struggle when they need to work together with humans. To fix this, the authors created a new system called iAgents that lets agents share information and work together more effectively. This system uses a special way of thinking (InfoNav) to help agents make good decisions and organize the information they get from humans. The paper also introduces a new way to test how well these agents can solve problems when working with humans, which is important for things like customer service or team projects. |
Keywords
* Artificial intelligence * Large language model
