Summary of Scaling Intelligent Agents in Combat Simulations For Wargaming, by Scotty Black et al.
Scaling Intelligent Agents in Combat Simulations for Wargaming
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 research paper proposes a novel approach to artificial intelligence (AI) for wargaming, leveraging machine learning to develop intelligent combat behavior. The authors aim to create an AI advisor capable of superhuman performance in complex simulation environments, ultimately serving as a decision-support tool for military planners. Building upon the successes of deep reinforcement learning and hierarchical reinforcement learning, the study investigates the application of HRL to develop agents that can excel in long-horizon, complex tasks characteristic of combat modeling and simulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is being developed to help with military planning. This means using machines to make decisions better than humans. Right now, computers are not good at this yet. The researchers want to create a super smart AI that can make great decisions in very complicated situations. They think they can do this by combining different types of machine learning and testing it on big simulations. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
