@inproceedings{rodriguezsummary,
title = {A Summary of Player Assessment in a Multi-UAV Mission Planning Serious Game},
author = {Víctor RodrÍguez-Fernández and Cristian Ramirez-Atencia and David Camacho},
url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/rodriguezsummary.pdf},
year = {2015},
date = {2015-07-31},
booktitle = {2st Congreso de la Sociedad Española para las Ciencias del Videojuego},
journal = {2st Congreso de la Sociedad Española para las Ciencias del Videojuego},
volume = {1394},
pages = {186--191},
abstract = {Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in different time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed
by players, comparing them against the optimal solutions obtained by a
Multi-Objective Optimization algorithm.},
keywords = {Mission Planning, Muli-UAV, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
Mission Planning for a large number of Unmanned Aerial Vehicles (UAVs) involves a set of locations to visit in different time intervals, and the actions that a vehicle must perform depending on its features and sensors. Analyzing how humans solve this problem is sometimes hard due to the complexity of the problem and the lack of data available. This paper presents a summary of a serious videogame-based framework created to assess the quality of the mission plans designed
by players, comparing them against the optimal solutions obtained by a
Multi-Objective Optimization algorithm.
@inproceedings{rodriguez2015multi,
title = {A multi-UAV Mission Planning videogame-based framework for player analysis},
author = {Victor Rodriguez-Fernandez and Cristian Ramirez-Atencia and David Camacho},
url = {http://aida.ii.uam.es/wp-content/uploads/2015/09/07257064.pdf},
year = {2015},
date = {2015-01-01},
booktitle = {Evolutionary Computation (CEC), 2015 IEEE Congress on},
pages = {1490--1497},
organization = {IEEE},
abstract = {The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.},
keywords = {Mission Planning, Multi-objective Optimization, Player Assessment, Serious Games, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.
