论文标题
培训数据集评估在多型地雷检测平台中进行决策
Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
论文作者
论文摘要
地雷检测等现实世界中的问题需要多种信息来源，以减少决策的不确定性。解决这些问题的一种新颖方法包括基于硬件和软件多代理系统的这项工作中介绍的分布式系统。为了达到高地雷检测率，我们评估了受过训练系统在训练和验证集之间的样品分布方面的性能。此外，还提供了数据集的一般解释，并介绍了由合作的多代理系统收集的样本，该系统开发了用于检测即兴爆炸装置的样本。结果表明，输入样本会影响输出决策的性能，而决策系统对传感器噪声的敏感性不太敏感，该智能系统从多样化且适当组织的训练集获得的智能系统中。
Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set.
