Summary of Scaling Artificial Intelligence For Digital Wargaming in Support Of Decision-making, by Scotty Black et al.
Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making
by Scotty Black, Christian Darken
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 tackles the pressing need to develop robust artificial intelligence (AI) for wargaming, enhancing all-domain awareness and decision-making speed. By pairing AI-enabled systems with human judgment, it aims to offer recommendations for novel courses of action, rapidly counter adversary actions, and potentially surpass human intelligence. The authors acknowledge the promising results of deep reinforcement learning but highlight the need for further research to scale AI for complex state-spaces characteristic of wargaming. To address this challenge, they propose a hierarchical reinforcement learning framework combining a multi-model approach and dimension-invariant observation abstractions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial Intelligence is like super-smart computer brain that helps us make better decisions fast. Imagine having a superhero sidekick that can analyze huge amounts of data and come up with new ideas in seconds! That’s what this research is all about – making AI even smarter to help us solve complex problems. The idea is not to replace humans, but to work together like a super team. Right now, AI is great at solving simple tasks, but it struggles when faced with really tough and complicated problems. To fix this, the researchers are creating a new way for AI to learn called hierarchical reinforcement learning. It’s like building a strong foundation of ideas that can be combined in many ways to solve different problems. |
Keywords
* Artificial intelligence * Reinforcement learning
