Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
This work addresses uncertainty reduction in landmine detection for safety applications, but it is incremental as it focuses on data set assessment rather than introducing new methods.
The study tackled the problem of improving landmine detection accuracy by analyzing how the distribution of training and validation samples affects decision-making performance in a multi-agent system, finding that a diverse and well-organized training set reduces sensitivity to sensor noise.
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.