Aerospace systems are complex systems with interacting disciplines and technologies. As a result, the Decision Makers (DMs) dealing with such problems are involved in balancing the multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. A variety of existing decision making methods are available to deal with this type of decision problems. The selection of a most appropriate decision making method is of particular importance since inappropriate decision methods are likely causes of misleading engineering design decisions. The research presented in this dissertation proposes a knowledge-based Multi-criteria Interactive Decision-making Advisor and Synthesis process (MIDAS), which can facilitate the selection of the most appropriate decision making method and which provides insight to the user for fulfilling different preferences. Once the most appropriate method is selected for the given problem, the advisor is also able to aid the DM to reach the final decision by following the rigorous problem solving procedure of the selected method. The MIDAS can also provide guidance as to the requirements needed to be fulfilled by a potentially new method for cases where no suitable method is found. In many other domains, such as complex system operation, decisions are often made in an environment with continuously changing situations. In addition, the decisions are usually completed based on uncertain or incomplete information due to the data availability and the environmental variation. This fact exacerbates the complexity of the decision making process because it results in the difficulties in perfectly and deterministically reasoning about the effects of the decisions and thus make it hard in determining the further decisions. In order to make proper decision and increase the system’s effectiveness, an advanced decision strategy is needed to capture the system’s dynamic characteristics and environmental uncertainty. An autonomous decision making advisor is developed to perform the real-time decision making under uncertainty. The development of the advisor system aims to solve a resource allocation problem to redistribute the limited resources to different agents under various scenarios and try to maximize the total rewards obtained from the resource allocation actions.

Nous proposons une nouvelle approche pour l'apprentissage de représentation parcimonieuse, où le but est de limiter le nombre de caractéristiques sélectionnées \textbf{par donnée}, résultant en un modèle que nous appellerons \textit{Modèle de parcimonie locale pour la classification} --- \textit{Datum-Wise Sparse Classification} (DWSC) en anglais. Notre approche autorise le fait que les caractéristiques utilisées lors de la classification peuvent être différentes d'une donnée à une autre: une donnée facile à classifier le sera ainsi en ne considérant que quelques caractéristiques, tandis que plus de caractéristiques seront utilisées pour les données plus complexes. Au contraire des approches traditionnelles de régularisation qui essaient de trouver un équilibre entre performance et parcimonie au niveau de l'ensemble du jeu de données, notre motivation est de trouver cet équilibre au niveau des données individuelles, autorisant une parcimonie moyenne plus élevée, pour une performance équivalente. Ce type de parcimonie est intéressant pour plusieurs raisons~: premièrement, nous partons du principe que les explications les plus simples sont toujours préférables~; deuxièmement, pour la compréhension des données, une représentation parcimonieuse par donnée fournit une information par rapport à la structure sous-jacente de celles-ci~: typiquement, si un jeu de données provient de deux distributions disjointes, DWSC autorise le modèle à choisir automatiquement de ne prendre en compte que les caractéristiques de la distribution génératrice de chaque donnée considérée. / This thesis introduces a body of work on sequential models for classification. These models allow for a more flexible and general approach to classification tasks. Many tasks ultimately require the classification of some object, but cannot be handled with a single atomic classification step. This is the case for tasks where information is either not immediately available upfront, or where the act of accessing different aspects of the object being classified may present various costs (due to time, computational power, monetary cost, etc.). The goal of this thesis is to introduce a new method, which we call datum-wise classification, that is able to handle these more complex classifications tasks by modelling them as sequential processes.