Summary of Improving Cooperation in Language Games with Bayesian Inference and the Cognitive Hierarchy, by Joseph Bills et al.
Improving Cooperation in Language Games with Bayesian Inference and the Cognitive Hierarchy
by Joseph Bills, Christopher Archibald, Diego Blaylock
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 explores cooperative games between two agents with varying levels of uncertainty about their teammate’s behavior. In language-based games, inaccurate assumptions can lead to poor performance. To address this issue, the authors propose a Bayesian approach that models coarse uncertainty in semantics using prior distributions and fine-grained uncertainty by adding noise to word embeddings. The method combines these aspects into a single prior distribution over possible partner types. The agents learn their partner’s behavior using Bayesian inference and maximize expected value through heuristic functions. The approach is tested on the Codenames game, demonstrating improved performance when semantics are uncertain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine playing a game with someone else, but you’re not sure what they’ll do next. This paper looks at how to make better choices in these situations. It’s like trying to understand what someone means when they say something – it can be tricky! The researchers developed a new way for computers (or “agents”) to learn from their partner’s moves and make good decisions even when things are unclear. They tested this approach on a popular word-guessing game called Codenames, and it worked really well! |
Keywords
» Artificial intelligence » Bayesian inference » Semantics
