In this work we introduce a structured signaling game, an extension of the classical signaling game with a similarity structure between meanings in the context, along with a variant of the Rational Speech Act (RSA) framework which we call structured-RSA (sRSA) for pragmatic reasoning in structured domains. We explore the behavior of the sRSA in the domain of color and show that pragmatic agents using sRSA on top of semantic representations, derived from the World Color Survey, attain efficiency very close to the information theoretic limit after only 1 or 2 levels of recursion. We also explore the interaction between pragmatic reasoning and learning in multi-agent reinforcement learning framework. Our results illustrate that artificial agents using sRSA develop communication closer to the information theoretic frontier compared to agents using RSA and just reinforcement learning. We also find that the ambiguity of the semantic representation increases as the pragmatic agents are allowed to perform deeper reasoning about each other during learning.

翻译：本文引入了一种结构化信号博弈（structured signaling game），这是经典信号博弈的扩展，其中语境中含义之间具有相似性结构；同时提出了一种理性言语行为（Rational Speech Act, RSA）框架的变体，称为结构化RSA（sRSA），用于结构化领域中的语用推理。我们探究了sRSA在颜色领域中的行为，并表明：在使用源于世界颜色调查（World Color Survey）的语义表示之上，基于sRSA的语用智能体在仅1至2层递归后便能达到接近信息论极限的效率。我们还探究了多智能体强化学习框架中语用推理与学习之间的交互作用。结果表明，与仅使用RSA和强化学习的智能体相比，采用sRSA的人工智能体所发展出的通信更接近信息论前沿。我们还发现，当语用智能体在学习过程中被允许对彼此进行更深层次的推理时，语义表示的模糊性会增加。