Multi-platform coordination and resource management in command and control

Depuis plusieurs années, nous constatons l'augmentation de l'utilisation des techniques d'agents et multiagent pour assister l'humain dans ses tâches. Ce travail de maîtrise se situe dans la même voie. Précisément, nous proposons d'utiliser les techniques multiagent de planification et de coordination pour la gestion de ressources dans les systèmes de commande et contrôle (C2) temps réel. Le problème particulier que nous avons étudié est la conception d'un système d'aide à la décision pour les opérations anti-aérienne sur les frégates canadiennes. Dans le cas où plusieurs frégates doivent se défendre contre des menaces, la coordination est un problème d'importance capitale. L'utilisation de mécanismes de coordination efficaces permet d'éviter les actions conflictuelles et la redondance dans les engagements. Dans ce mémoire, nous présentons quatre mécanismes de coordination basés sur le partage de tâche. Trois sont basés sur les communications : la coordination centrale, le Contract Net, la coordination similaire à celle proposée par Brown; tandis que la défense de zone est basée sur les lois sociales. Nous exposons enfin les résultats auxquels nous sommes arrivés en simulant ces différents mécanismes.The use of agent and multiagent techniques to assist humans in their daily routines has been increasing for many years, notably in Command and Control (C2) systems. This thesis is is situated in this domain. Precisely, we propose to use multiagent planning and coordination techniques for resource management in real-time \acs{C2} systems. The particular problem we studied is the design of a decision-support for anti-air warfare on Canadian frigates. In the case of several frigates defending against incoming threats, multiagent coordination is a complex problem of capital importance. Better coordination mechanisms are important to avoid redundancy in engagements and inefficient defence caused by conflicting actions. In this thesis, we present four different coordination mechanisms based on task sharing. Three of these mechanisms are based on communications: central coordination, Contract Net coordination and Brown coordination, while the zone defence coordination is based on social laws. Finally, we expose the results obtained while simulating these various mechanisms

Techniques for the allocation of resources under uncertainty

L’allocation de ressources est un problème omniprésent qui survient dès que des ressources limitées doivent être distribuées parmi de multiples agents autonomes (e.g., personnes, compagnies, robots, etc). Les approches standard pour déterminer l’allocation optimale souffrent généralement d’une très grande complexité de calcul. Le but de cette thèse est de proposer des algorithmes rapides et efficaces pour allouer des ressources consommables et non consommables à des agents autonomes dont les préférences sur ces ressources sont induites par un processus stochastique. Afin d’y parvenir, nous avons développé de nouveaux modèles pour des problèmes de planifications, basés sur le cadre des Processus Décisionnels de Markov (MDPs), où l’espace d’actions possibles est explicitement paramétrisés par les ressources disponibles. Muni de ce cadre, nous avons développé des algorithmes basés sur la programmation dynamique et la recherche heuristique en temps-réel afin de générer des allocations de ressources pour des agents qui agissent dans un environnement stochastique. En particulier, nous avons utilisé la propriété acyclique des créations de tâches pour décomposer le problème d’allocation de ressources. Nous avons aussi proposé une stratégie de décomposition approximative, où les agents considèrent des interactions positives et négatives ainsi que les actions simultanées entre les agents gérants les ressources. Cependant, la majeure contribution de cette thèse est l’adoption de la recherche heuristique en temps-réel pour l’allocation de ressources. À cet effet, nous avons développé une approche basée sur la Q-décomposition munie de bornes strictes afin de diminuer drastiquement le temps de planification pour formuler une politique optimale. Ces bornes strictes nous ont permis d’élaguer l’espace d’actions pour les agents. Nous montrons analytiquement et empiriquement que les approches proposées mènent à des diminutions de la complexité de calcul par rapport à des approches de planification standard. Finalement, nous avons testé la recherche heuristique en temps-réel dans le simulateur SADM, un simulateur d’allocation de ressource pour une frégate.Resource allocation is an ubiquitous problem that arises whenever limited resources have to be distributed among multiple autonomous entities (e.g., people, companies, robots, etc). The standard approaches to determine the optimal resource allocation are computationally prohibitive. The goal of this thesis is to propose computationally efficient algorithms for allocating consumable and non-consumable resources among autonomous agents whose preferences for these resources are induced by a stochastic process. Towards this end, we have developed new models of planning problems, based on the framework of Markov Decision Processes (MDPs), where the action sets are explicitly parameterized by the available resources. Given these models, we have designed algorithms based on dynamic programming and real-time heuristic search to formulating thus allocations of resources for agents evolving in stochastic environments. In particular, we have used the acyclic property of task creation to decompose the problem of resource allocation. We have also proposed an approximative decomposition strategy, where the agents consider positive and negative interactions as well as simultaneous actions among the agents managing the resources. However, the main contribution of this thesis is the adoption of stochastic real-time heuristic search for a resource allocation. To this end, we have developed an approach based on distributed Q-values with tight bounds to diminish drastically the planning time to formulate the optimal policy. These tight bounds enable to prune the action space for the agents. We show analytically and empirically that our proposed approaches lead to drastic (in many cases, exponential) improvements in computational efficiency over standard planning methods. Finally, we have tested real-time heuristic search in the SADM simulator, a simulator for the resource allocation of a platform

Air Force Institute of Technology Research Report 2001

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

A Two-Step Optimisation Method for Dynamic Weapon Target Assignment Problem

International audienceno abstrac

Air Force Institute of Technology Research Report 2000

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, and Engineering Physics

Analyzing the Interdiction of Sea-Borne Threats Using Simulation Optimization

Worldwide, maritime trade accounts for approximately 80% of all trade by volume and is expected to double in the next twenty years. Prior to September 11, 2001, Ports, Waterways and Coastal Security (PWCS) was afforded only 1 percent of United States Coast Guard (USCG) resources. Today, it accounts for nearly 22 percent of dedicated USCG resources. Tactical assessment of resource requirements and operational limitations on the PWCS mission is necessary for more effective management of USCG assets to meet the broader range of competing missions. This research effort involves the development and validation of a discrete-event simulation model of the at-sea vessel interdiction process utilizing USCG deepwater assets. A discrete-event simulation model of the interdiction, control and boarding, and inspection processes has been developed and validated. Through a simulation optimization approach, our research utilizes the efficiency of a localized search algorithm interfaced with the simulation model to allocate USCG resources in the interception, boarding, and inspection processes with the objective of minimizing overall process time requirements. The model is tested with actual USCG data to gain insight on the development of efficient and effective interdiction operations

Valuing Persistent ISR Resources

AFCEA-GMU C4I Center Symposium, Challenges in C4I, George Mason University, Fairfax, VA., May 25This paper describes how to optimize PISR resources to maximize VIRT

Air Force Institute of Technology Research Report 2006

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Air Force Institute of Technology Research Report 2007

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A modified greedy algorithm for the task assignment problem.

Assigning workers to tasks in an efficient and cost effective manner is a problem that nearly every company faces. This task assignment problem can be very time consuming to solve optimally. This difficulty increases as problem size increases. Most companies are large enough that it isn\u27t feasible to find an optimal assignment; therefore a good heuristic method is needed. This project involved creating a new heuristic to solve this problem by combining the Greedy Algorithm with the Meta-RaPS method. The Greedy Algorithm is a near-sighted assignment procedure that chooses the best assignment at each step until a full solution is found. Although the Greedy Algorithm finds a good solution for small to medium sized problems, introducing randomness using the meta-heuristic Meta-RaPS results in a better solution. The new heuristic runs 5000 iterations and reports the best solution. The final Excel® VBA program solves a small sized problem in less than one minute, and is within 10% of the optimal solution, making it a good alternative to time consuming manual assignments. Although larger, more realistic problems will take longer to solve, good solutions will be available in a fraction of the time compared to solving them optimally