Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform

• URL: http://arxiv.org/abs/2004.05380v1
• Date: Sat, 11 Apr 2020 12:05:30 GMT
• Title: Training Data Set Assessment for Decision-Making in a Multiagent Landmine Detection Platform
• Authors: Johana Florez-Lozano, Fabio Caraffini, Carlos Parra and Mario Gongora
• Abstract summary: 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. We evaluate the performance of a trained system over the distribution of samples between training and validation sets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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