通讯式学习——统一的机器学习模式

Communicative Learning:A Unified Learning Formalism

在本文中,我们提出了一种沟通学习(CL)形式主义,它统一了现有的机器学习范式,如被动学习、主动学习、算法教学等,并促进了新的学习方法的发展。这种形式主义源于人类的合作交流,将学习视为一种交流过程,并将教育学与新兴的机器学习领域相结合。在机器学习中,除了随机抽样数据之外,教学洞察力有助于采用替代信息源,例如由乐于助人的老师提供的有意信息。更具体地说,在CL中,教师和学生相互协作交换信息,以传递和获取一定的知识。每个主体都有一个心智,心智包括主体的知识、效用和心理动态。为了建立有效的沟通,每个代理还需要估计其合作伙伴的想法。我们定义了足以进行这种递归建模的表达性心理表征和学习公式,这使CL具有与人类相当的学习效率。我们演示了CL在几个原型协作任务中的应用,并说明了这种形式化允许学习协议超越Shannon的通信限制。最后,我们通过提出学习的层次结构和定义学习的停止问题来展示我们对学习基础的贡献。

In this article,we propose a communicative learning(CL)formalism that unifies existing machine learning paradigms,such as passive learning,active learning,algorithmic teaching,and so forth,and facilitates the development of new learning methods.Arising from human cooperative communication,this formalism poses learning as a communicative process and combines pedagogy with the burgeoning field of machine learning.The pedagogical insight facilitates the adoption of alternative information sources in machine learning besides randomly sampled data,such as intentional messages given by a helpful teacher.More specifically,in CL,a teacher and a student exchange information with each other collaboratively to transmit and acquire certain knowledge.Each agent has a mind,which includes the agent’s knowledge,utility,and mental dynamics.To establish effective communication,each agent also needs an estimation of its partner’s mind.We define expressive mental representations and learning formulation sufficient for such recursive modeling,which endows CL with human-comparable learning efficiency.We demonstrate the application of CL to several prototypical collaboration tasks and illustrate that this formalism allows learning protocols to go beyond Shannon’s communication limit.Finally,we present our contribution to the foundations of learning by putting forth hierarchies in learning and defining the halting problem of learning.