Approches environnement-centrées pour la simulation de systèmes multi-agents: Pour un déplacement de la complexité des agents vers l'environnement

This habilitation thesis synthesizes research works which are mainly related to the field of Multi-Agent Based Simulation (MABS). MABS is a general framework for modeling and experimenting with systems in which the dynamics emerges from local interactions among individuals (autonomous agents). Examples of use range from the study of natural systems (e.g. ant colonies, crowds or traffic jams) to the engineering of artificial ones (e.g., collective robotics, distributed artificial intelligence-based softwares). To this end, MABS modeling represents the behavior of individuals, their environment and interactions, so that global dynamics can be computed and studied from the bottom up. In this context, we have been investigating research on the theory and practice of MABS from two different perspectives : (1) the design of generic abstractions dedicated to the modeling of multi-agent dynamics (e.g., the IRM4S model) and (2) the engineering of MABS (MaDKit and TurtleKit platforms). Besides, we have been experimenting with MABS in different application domains such as image processing, video games, and collective robotics. Contrary to approaches that put the emphasis on the agent behaviors, all these works have been done by considering the environment of the agents as a first order abstraction. In this thesis, we first reflect upon the research we have conducted according to this perspective. Next, we show how we actually use this perspective to propose an original approach for using General-Purpose processing on Graphics Processing Units (GPGPU) within MABS, and then present the research perspectives related to our positioning.Les travaux de recherche synthétisés dans ce mémoire s’inscrivent principalement dans le domaine de la modélisation et de la simulation de systèmes multi-agents (SMA). La simulation multi-agents met en œuvre des modèles où les individus, leur environnement et leurs interactions sont directement représentés. Dans ces modèles, chaque individu –agent autonome– possède son propre comportement et produit ses actions en fonction d’une perception locale de son environnement. Ainsi, la simulation multi-agents est utilisée pour étudier des systèmes naturels comme les colonies de fourmis, les dynamiques de foules ou le trafic urbain, mais aussi pour concevoir des systèmes artificiels, par exemple dans le cadre de la robotique collective ou le développement de logiciels basés sur de l’intelligence artificielle distribuée. Dans ce cadre, nos recherches ont porté sur des problématiques liées à la modélisation de simulations multi-agents, avec la proposition de modèles formels et conceptuels (e.g. le modèle IRM4S) et d’outils logiciels génériques (plates-formes MaDKit et TurtleKit), et sur leur utilisation dans divers domaines tels que le jeu vidéo, le traitement numérique de l’image ou la robotique collective. Contrairement aux approches centrées sur la conception des comportements individuels, dans ces travaux l’environnement des agents est considéré comme une abstraction de premier ordre. Dans ce mémoire, nous dressons tout d’abord un bilan de nos recherches en argumentant l’intérêt d’une telle démarche pour les modèles multi-agents. Nous montrons ensuite comment celle-ci nous a récemment permis de proposer une approche originale dans le cadre de l’utilisation du calcul haute performance sur carte graphique (GPGPU) pour la simulation de SMA, avant de présenter les perspectives de recherche associées à notre positionnement

A Study of Bi-Directional Reflectance Distribution Functions and Their Effects on Infrared Signature Models

Since 2004, AFIT has been developing a trend-analysis tool to assess large commercial aircraft infrared (LCAIR) signatures. In many cases, this code predicted signatures to within 10% of measured data. However, other results indicated that the single-bounce, specular-reflection algorithm being used failed to adequately simulate interactions between aircraft parts where either the specular component is dominated by diffuse reflection or part-to-part multiple-bounce reflections contribute significantly to the signature. This research incorporates Bi-Directional Reflectance Distribution Functions (BRDF\u27s) and multiple-bounce calculations into the LCAIR model. A physical aircraft model was constructed from aluminum, and measurements were taken before and after a surface treatment in gloss black paint. The Sandford-Robertson model is used to parameterize the BRDF\u27s of both the bare aluminum and gloss black paint. Since the most efficient method of integrating a BRDF depends upon the reflectance distribution of the aircraft material, the sampling resolution of the BRDF integral is crucial to an accurate simulation. Additionally, care is taken to ensure that the integration of the hemispherical irradiance onto each facet of the computational model is sampled at a sufficient resolution to achieve convergence in the solution. Simulations in the mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands validate both the previous specular reflectance simplification for the gloss black simulations and the failure of the previous algorithm for the highly reflective bare aluminum. The necessity of considering multiple bounces in the simulation is also demonstrated amongst part-to-part reflections near the wing root, where three or four bounces are required for the solution to converge. Finally, three scenarios simulating a man-portable air defense system (MAN-PADS) system engaging an Airbus A340-300 aircraft landing at a generic airport are performed