Narrow artificial intelligence with machine learning for real-time estimation of a mobile agent's location using hidden markov models

被引：3

作者：

Beaulac, Cédric ^{[1
]}

Larribe, Fabrice ^{[1
]}

机构：

[1] Département de Mathématiques, Université du Québec Á Montréal, 201 avenue du Président-Kennedy, Montréal,QC, Canada

来源：

International Journal of Computer Games Technology | 2017年 / 2017卷

关键词：

Supervised learning - Learning algorithms - Mobile agents;

D O I：

10.1155/2017/4939261

中图分类号：

学科分类号：

摘要：

We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent's position using the forward algorithm. Second, it uses the Baum-Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method. © 2017 Cédric Beaulac and Fabrice Larribe.

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