Managing power amongst a group of networked embedded fpgas using dynamic reconfiguration and task migration

Small unpiloted aircraft (UAVs) each have limited power budgets. If a group (swarm) of small UAVs is organized to perform a common task such as geo-location then it is possible to share the total power across the group by introducing task mobility inside the group supported by an ad hoc wireless network (where the communication encoding/decodeing is also done on fpgas). In this presentation I will describe research into the construction of a distributed operating system where partial dynamic reconfiguration and network mobility are combined so that fpga tasks can be moved to make the best use of the total power available in a swarm of UAVs

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

Capturing Agent Autonomy in Roles and XML

A key question in the field of agent-oriented software engineering is how the kind and extent of autonomy owned by computational agents can be appropriately captured. As long as this question is not answered convincingly, it is very unlikely that agent-oriented software (having "autonomy" as a real property rather than just a catchy label) gets broadly accepted in industry and commerce. In particular, in order to be of practical value an answer to this question has to come in form of concrete techniques which enable developers of agent-oriented software to precisely capture the scope of behavioral freedom and self-control they want to concede to a computational agent. This paper describes two such techniques. First, a formal schema called RNS for specifying the boundaries of autonomous agent behavior. This schema is conceptually grounded in sociological role theory, and employs the concepts of role, norm and sanction to capture agent autonomy. What makes RNS particularly valuable and distinct from related autonomy specification approaches is, among other things, its strong expressiveness and high precision. Second, a software tool called XRNS which enables developers to easily generate RNS-based autonomy specifications in XML format. Encoded in XML, these specifications are easily accessible to all stakeholders in an agent-oriented software under development, and can be even processed directly by XML enabled computational agents