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

A multiagent task associated mdp (mtamdp) approach to resource allocation

We are interested in contributing to solving effectively the a specific type of real-time stochastic resource allocation problem, which is known as NP-Hard, of which the main distinction is the high number of possible interacting actions to execute in a group of tasks. To address this complex resource management problem, we propose an adaptation of the Multiagent Markov Decision Process (MMDP) model which centralizes the computation of interacting resources. This adaptation is called Multiagent Task Associated Markov Decision Process (MTAMDP) and produces a near-optimal solution policy in a much lower time than a standard MMDP approach. In a MTAMDP, a planning agent computes a policy for each resource, and are coordinated by a central agent. MTAMDPs enables to practically solve our NP-Hard problem. 1

A Frigate Movement Survival Agent-Based Approach

The position of a frigate to face some threats can augment its survival chances and therefore it is important to investigate this aspect in order to determine how a frigate can position itself during an attack. To achieve that, we propose a first method based on the Bayesian movement, performed by a learning agent, which determines the optimal positioning of the frigate by dividing the defense area into six sectors for weapon engagement and then, it makes efficient use of all the weapons available by using the sectors. The second method that we propose is called Radar Cross-Section Reduction (RCSR) movement and, it aims at reducing the exposed surface of the frigate to incoming threats before their locking phase is over. Preliminary results on these two methods are presented and discussed. Finally, an implementation of a meta-level agent which would make efficient use of both complementary methods is suggested