Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
Abstract
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.
Cite
@article{arxiv.2004.05380,
title = {Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform},
author = {Johana Florez-Lozano and Fabio Caraffini and Carlos Parra and Mario Gongora},
journal= {arXiv preprint arXiv:2004.05380},
year = {2020}
}