Summary of Swarmbrain: Embodied Agent For Real-time Strategy Game Starcraft Ii Via Large Language Models, by Xiao Shao et al.
SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models
by Xiao Shao, Weifu Jiang, Fei Zuo, Mengqing Liu
First submitted to arxiv on: 31 Jan 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 This paper investigates the effectiveness of large language models (LLMs) in executing real-time strategy war tasks within the StarCraft II gaming environment. The authors introduce SwarmBrain, an embodied agent that leverages LLMs for real-time strategy implementation. SwarmBrain consists of two key components: Overmind Intelligence Matrix and Swarm ReflexNet. The former is designed to orchestrate macro-level strategies from a high-level perspective, while the latter employs a condition-response state machine framework to enable expedited tactical responses. Experimental results show that SwarmBrain can conduct economic augmentation, territorial expansion, and tactical formulation, ultimately achieving victory against Computer players set at different difficulty levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be used in video games like StarCraft II. The authors created a special AI called SwarmBrain that uses these models to make decisions. SwarmBrain has two parts: one part thinks about the big picture and makes plans, while the other part acts quickly to respond to situations. In a game against a computer-controlled opponent, SwarmBrain was able to gather resources, expand its territory, and win battles. |
