Abstract
Within Reinforcement Learning, there is a fledgling approach to conceptualizing the environment in terms of predictions. Central to this predictive approach is the assertion that it is possible to construct ontologies in terms of predictions about sensation, behaviour, and time---to categorize the world into entities which express all aspects of the world using only predictions. This construction of ontologies is integral to predictive approaches to machine knowledge where objects are described exclusively in terms of how they are perceived. In this paper, we ground the Pericean model of semiotics in terms of Reinforcement Learning Methods, describing Peirce's Three Categories in the notation of General Value Functions. Using the Peircean model of semiotics, we demonstrate that predictions alone are insufficient to construct an ontology; however, we identify predictions as being integral to the meaning-making process. Moreover, we discuss how predictive knowledge provides a particularly stable foundation for semiosis\textemdash the process of making meaning\textemdash and suggest a possible avenue of research to design algorithmic methods which construct semantics and meaning using predictions.
Abstract (translated)
在强化学习中，有一种新的方法可以根据预测来概念化环境。这种预测方法的核心是这样一种断言，即可以根据对感觉、行为和时间的预测来构造本体论，将世界分类为只使用预测来表达世界各个方面的实体。本体的这种构造是机器知识预测方法的一个组成部分，在机器知识预测方法中，对象是专门按照感知方式来描述的。本文从强化学习的角度出发，建立了符号学的佩里西亚模型，用一般值函数表示法描述了皮尔斯的三个范畴。使用皮尔斯符号学模型，我们证明了预测本身不足以构建本体论；然而，我们将预测识别为意义生成过程的整体。此外，我们还讨论了预测知识如何为SythysTeXTEMDASH提供了一个特别稳定的基础，即意义的加工过程，并提出了使用预测来构造语义和意义的算法方法的研究的可能途径。
URL
https://arxiv.org/abs/1904.09023
