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.
翻译:在加强学习内部,有一种从预测角度将环境概念化的新兴方法。这种预测方法的核心是,在预测感知、行为和时间方面,可以将世界划分为只使用预测来表达世界各个方面的实体。这种肿瘤学的构建对于预测机器知识的方法来说是不可或缺的,因为对机器知识的预测方法只能从对物体的认知的角度来描述物体。在本文中,我们用强化学习方法将Peicean半科学模型作为基础,用一般价值函数的标记来描述Peirce的三种类别。我们利用Peirce半科学模型来证明,单凭预测不足以构建一个肿瘤学;然而,我们确定预测是意义形成过程的组成部分。此外,我们讨论了预测性知识如何为确定含义的过程提供特别稳定的基础。我们提出一种可能的研究途径,以设计算法方法,用预测来构建语义和含义。