狭义人工智能结合机器学习的隐马尔可夫模型实时的移动代理位置估计
Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agents Location Using Hidden Markov Models
摘要：
我们建议使用有监督的机器学习技术来实时的跟踪移动代理的位置。隐马尔可夫模型用于构建人工智能，该智能估计在定义环境中移动的移动目标的未知位置。这种狭义的人工智能执行两个不同的任务。首先，它提供了实时的估计移动代理的位置使用的前向算法。其次，它使用Baum-Welch算法作为统计学习工具来获得移动目标的知识。最后，提出了一个实验环境，即一个视频游戏，我们用来测试我们的人工智能。我们提出的统计和图形结果来说明我们的方法的效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
