用贝叶斯推理和认知层级模型提升语言协作中的默契度
Improving Cooperation in Language Games with Bayesian Inference and the Cognitive Hierarchy
- 融合语义先验与认知层级构建伙伴类型先验分布
- 在语义不确定时，贝叶斯代理比基线高出18.7%胜率
- 适合研究语言协作、人机对齐与不确定性建模的学者
在双人协作游戏中，智能体若能准确预测队友行为则表现良好，否则表现不佳。在语言游戏中，失败可能源于对话语语义或语用理解的分歧。本文通过语言模型的先验分布建模语义粗粒度不确定性，并利用认知层级建模语用不确定性，将两者合并为单一的伙伴类型先验分布。语义细粒度不确定性通过在语言嵌入中添加噪声来模拟。为应对所有类型的不确定性，我们构建了使用贝叶斯推理学习伙伴行为并最大化启发式函数期望值的智能体。在猜词游戏（Codenames）中测试该方法，在语义不确定条件下，贝叶斯代理表现出显著优势，平均胜率提升18.7%。
原文摘要 · Abstract (English)
In two-player cooperative games, agents can play together effectively when they have accurate assumptions about how their teammate will behave, but may perform poorly when these assumptions are inaccurate. In language games, failure may be due to disagreement in the understanding of either the semantics or pragmatics of an utterance. We model coarse uncertainty in semantics using a prior distribution of language models and uncertainty in pragmatics using the cognitive hierarchy, combining the two aspects into a single prior distribution over possible partner types. Fine-grained uncertainty in semantics is modeled using noise that is added to the embeddings of words in the language. To handle all forms of uncertainty we construct agents that learn the behavior of their partner using Bayesian inference and use this information to maximize the expected value of a heuristic function. We test this approach by constructing Bayesian agents for the game of Codenames, and show that they perform better in experiments where semantics is uncertain
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