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Learning from Each Other

机译：互相学习

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Since its inception, the field of machine learning has seen the advent of several learning paradigms, designed to frame the issues central to the learning activity, provide effective learning methods, and investigate the power and limitations inherent to the process of successful learning. In this article, we propose a formalization that underlies the key concepts of many such paradigms and discuss their relevance to scientific discovery, with the aim of assessing what scientists can expect from machines designed to assist them in their quest for the discovery of valid laws. We illustrate the formalization on several variations of a card game, and highlight the differences that paradigms impose on learners, as well as the assumptions they make on the nature of the learning process. We then use the formalization to describe a multi-agent interaction protocol, that has been inspired by these paradigms and that has been validated recently on some groups of agents. Finally, we propose extensions to this protocol.
机译：自成立以来，机器学习领域已经出现了几种学习范式，这些范式旨在框架学习活动的核心问题，提供有效的学习方法，并研究成功学习过程固有的力量和局限性。在本文中，我们提出了一种形式化的解释，该形式化是许多这类范例的关键概念的基础，并讨论了它们与科学发现的相关性，目的在于评估科学家对旨在帮助他们寻求有效法律的机器所期望的东西。我们举例说明了纸牌游戏的几种变体的形式化，并强调了范式强加给学习者的差异，以及他们对学习过程的性质所作的假设。然后，我们使用形式化描述一种多代理交互协议，该协议已受到这些范例的启发，并且最近在某些代理组上得到了验证。最后，我们提出对该协议的扩展。

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