Modeling the behavior of unskilled users in a multi-uav simulation environment

Abstract

The use of Unmanned Aerial Vehicles (UAVs) has been growing over the last few years. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process and focus on a wider range of candidates, including inexperienced users in the field, in order to detect skilled-potential operators. This paper uses data from the interactions of multiple unskilled users in a simple multi-UAV simulator to create a behavioral model through the use of Hidden Markov Models (HMMs). An optimal HMM is validated and analyzed to extract common behavioral patterns among these users, so that it is proven that the model represents correctly the novelty of the users and may be used to detect and predict behaviors in multi-UAV systems.

Publication
International Conference on Intelligent Data Engineering and Automated Learning
Antonio Gonzalez-Pardo
Antonio Gonzalez-Pardo
PhD Computer Science

Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.