Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent's Location Using Hidden Markov Models. (14th February 2017)

• Record Type: Journal Article
• Authors: Beaulac, Cédric; Larribe, Fabrice
• Other Names: Katchabaw Michael J. Academic Editor.
• Is Part Of: International journal of computer games technology. Volume 2017(2017)
• Publication Date: 2017-02-14
• Subjects: Computer games -- Design -- Periodicals; Computer games -- Programming -- Periodicals; Jeux d'ordinateur; Jeux d'ordinateur -- Programmation; Computer games -- Design; Computer games -- Programming; Electronic journals; Periodicals; 794.81
• DOI: 10.1155/2017/4939261
• Languages: English
• ISSNs: 1687-7047

Abstract

Abstract : 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.