多智能体地雷探测平台中用于决策的训练数据集评估
Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
摘要：
地雷探测等现实问题需要多种信息来源，以减少决策的不确定性。一种新的方法来解决这些问题，包括分布式系统，在这项工作中提出的硬件和软件的多智能体系统的基础上。为了实现高地雷检测率，我们评估了训练系统在训练集和验证集之间样本分布的性能。此外，还对数据集作了一般性解释，介绍了为探测简易爆炸装置而开发的合作多主体系统收集的样本。结果表明，输入样本影响输出决策的性能，决策系统可以不太敏感的传感器噪声与智能系统从不同的和适当组织的训练集。
英文摘要：
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.
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