A mathematical theory of cooperative communication
A mathematical theory of cooperative communication
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发表时间：
2019-10
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通讯作者：
Pei Wang;Junqi Wang;P. Paranamana;Patrick Shafto
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作者：
Pei Wang;Junqi Wang;P. Paranamana;Patrick Shafto
合作交流在人类认知、语言、发展、文化和人机交互理论中发挥着核心作用。先前的合作通信模型本质上是算法性的，并没有阐明为什么合作可以产生有效的信念传播，以及由于代理信念之间的差异可能会出现哪些限制。通过与最优传输理论的联系，我们建立了合作通信的数学框架。我们推导出先验模型作为特殊情况、信念转移计划的统计解释以及稳健性和不稳定性的证明。计算模拟支持并阐述了我们的理论结果，并证明了其适合人类行为。结果表明，协作通信可以实现有效、稳健的信念传递，这是解释人类学习成就和改善人机交互所必需的。
Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise due to differences between beliefs of agents. Through a connection to the theory of optimal transport, we establishing a mathematical framework for cooperative communication. We derive prior models as special cases, statistical interpretations of belief transfer plans, and proofs of robustness and instability. Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. The results show that cooperative communication provably enables effective, robust belief transmission which is required to explain feats of human learning and improve human-machine interaction.