Application of Machine Learning in Human-Computer Intelligent Confrontation

Abstract

Human-computer intelligent confrontation is a widely used technique in a lot of game scenarios, and the artificial intelligence makes a lot of success in different fields. The level of artificial intelligence improves quickly with the development of machine learning. Machine learning refers to enhancing the capabilities of artificial intelligence through the interaction of machines and data. Among all kinds of machine learning, the most famous one is deep reinforced learning. This technology is currently widely used in human-computer confrontations. This article introduces the technical principles of machine learning and reviews the development history of machine learning theory. Besides, this article introduces the application of machine learning in the two important competitive fields of Go and Texas Hold’em, as well as its development and achievements in these two areas. This article can enable other scholars to have a more comprehensive and systematic understanding of machine learning and its applications in Go and Texas Hold’em.