Summary of Strategy Game-playing with Size-constrained State Abstraction, by Linjie Xu et al.
Strategy Game-Playing with Size-Constrained State Abstraction
by Linjie Xu, Diego Perez-Liebana, Alexander Dockhorn
First submitted to arxiv on: 12 Aug 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 tackles the challenge of AI playing strategy games by proposing a novel approach to state abstraction. The current state-of-the-art in this area relies on reducing the search space, but evaluating the quality of these abstractions is difficult. This leads to abandoning the abstraction mid-search to avoid biasing the search towards a local optimum. The proposed size-constrained state abstraction (SCSA) addresses this issue by limiting the maximum number of nodes being grouped together. Experimental results show that SCSA outperforms previous methods and yields robust performance across different games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is trying to get better at playing strategy games, but it’s hard because there are many things to consider. To make it easier, researchers use something called state abstraction. This helps reduce the search space, but it’s tricky to know when to stop using this method so that AI doesn’t get stuck in a bad spot. A new way of doing state abstraction is proposed in this paper, which limits how much information is combined at once. The results show that this new approach does better than old ones and works well across different games. |
