Summary of Affordable Generative Agents, by Yangbin Yu et al.
Affordable Generative Agents
by Yangbin Yu, Qin Zhang, Junyou Li, Qiang Fu, Deheng Ye
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 Affordable Generative Agents (AGA), a framework for creating believable and low-cost interactions between language models and environments or other agents. The authors develop learned policies to replace repetitive inferences in language models, allowing for efficient generation of agent-environment interactions. Additionally, they model social relationships between agents and compress dialogue information to reduce the cost of inter-agent interactions. Experimental results demonstrate the effectiveness and efficiency of AGA. Furthermore, the paper delves into the mechanisms underlying emergent believable behaviors in language model-based agents, showing that they can only generate a finite number of behaviors within fixed environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates agents that seem real and can interact with each other and their environment in a believable way. The challenge is that these interactions take a lot of processing power and memory. To solve this problem, the authors developed a new framework called AGA (Affordable Generative Agents). They found ways to make the agents more efficient by using learned policies instead of repetitive calculations. They also made it possible for agents to interact with each other in a way that is realistic and uses less processing power. The results show that this works well, and we can learn more about how these agents work. |
Keywords
* Artificial intelligence * Language model
