Distributed constraint programming with agents

Many combinatorial optimization problems lend themselves to be modeled as distributed constraint optimization problems (DisCOP). Problems such as job shop scheduling have an intuitive matching between agents and machines. In distributed constraint problems, agents control variables and are connected via constraints. We have equipped these agents with a full constraint solver. This makes it possible to use global constraint and advanced search schemes. By empowering the agents with their own solver, we overcome the low performance that often haunts distributed constraint satisfaction problems (DisCSP). By using global constraints, we achieve far greater pruning than traditional DisCSP models. Hence, we dramatically reduce communication between agents. Our experiments show that both global constraints and advanced search schemes are necessary to optimize job shop schedules using DisCSP

Safe and Secure Support for Public Safety Networks

International audienceAs explained by Tanzi et al. in the first volume of this book, communicating and autonomous devices will surely have a role to play in the future Public Safety Networks. The “communicating” feature comes from the fact that the information should be delivered in a fast way to rescuers. The “autonomous” characteristic comes from the fact that rescuers should not have to concern themselves about these objects: they should perform their mission autonomously so as not to delay the intervention of the rescuers, but rather to assist them efficiently and reliably.</p

Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms

Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.Comment: This is a preprint version of the article submitted and published in Soft Computin