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

翻译：在双玩家合作游戏中，当智能体对队友行为具有准确预判时能实现高效协作，但预判失准则可能导致表现欠佳。在语言游戏中，协作失败可能源于对话语语义或语用理解的分歧。我们通过语言模型的先验分布对语义的粗粒度不确定性进行建模，并利用认知层级对语用不确定性进行建模，将两方面结合为对可能合作伙伴类型的单一先验分布。语义的细粒度不确定性则通过向语言词汇嵌入添加噪声进行建模。为处理所有形式的不确定性，我们构建了通过贝叶斯推理学习伙伴行为、并利用该信息最大化启发式函数期望值的智能体。我们通过构建适用于Codenames游戏的贝叶斯智能体对该方法进行测试，实验表明其在语义不确定场景中表现更优。