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    FATMAS: a methodology to design fault-tolerant multi-agent systems

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    Un systĂšme multi-agent (SMA) est un systĂšme dans lequel plusieurs agents opĂšrent et interagissent. Chaque agent a la responsabilitĂ© d’exĂ©cuter des tĂąches. Cependant, chaque agent, pour diverses raisons, peut rencontrer des problĂšmes pendant l’exĂ©cution de ses tĂąches ; ce qui peut induire un disfonctionnement du SMA. Cependant, le SMA doit ĂȘtre en mesure de dĂ©tecter les sources de problĂšms (d’erreurs) afin de les contrĂŽler et ainsi continuer son exĂ©cution correctement. Un tel SMA est appelĂ© un SMA tolĂ©rant aux fautes. Il existe deux types de sources d’erreurs pour un agent : les erreurs causĂ©es par son environnment et les erreurs dĂ»es Ă  sa programmation. Dans la littĂ©rature, il existe plusieurs techniques qui traitent des erreurs de programmation au niveau des agents. Cependant, ces techniques ne traitent pas des erreurs causĂ©es par l’environnement de l’agent. Tout d’abord, nous distinguons entre l’environnment d’un agent et l’environnement du SMA. L’environnement d’un agent reprĂ©sente toutes les composantes matĂ©rielles ou logicielles que l’agent ne peut contrĂŽler mais avec lesquelles il interagit. Cependant, l’environnment du SMA reprĂ©sente toutes les composantes que le systĂšme ne contrĂŽle pas mais avec lesquelles il interagit. Ainsi, le SMA peut contrĂŽler certaines des composantes avec lesquelles un agent interagit. Ainsi, une composante peut appartenir Ă  l’environnement d’un agent et ne pas appartenir Ă  l’environnement du systĂšme. Dans ce travail, nous prĂ©sentons une mĂ©thodologie de conception de SMA tolĂ©rants aux fautes, nommĂ©e FATMAS, qui permet au concepteur du SMA de dĂ©tecter et de corriger, si possible, les erreurs causĂ©es par les environnements des agents. Cette mĂ©thodologie permettra ainsi de dĂ©limiter la frontiĂšre du SMA de son environnement avec lequel il interagit. La frontiĂšre du SMA est dĂ©terminĂ©e par les diffĂ©rentes composantes (matĂ©rielles ou logicielles) que le systĂšme contrĂŽle. Ainsi, le SMA, Ă  l’intĂ©rieur de sa frontiĂšre, peut corriger les erreurs provenant de ses composantes. Cependant, le SMA n’a aucun contrĂŽle sur toutes les composantes opĂ©rant dans son environnement. La mĂ©thodologie, que nous proposons, doit couvrir les trois premiĂšres phases d’un dĂ©veloppement logiciel qui sont l’analyse, la conception et l’implĂ©mentation tout en intĂ©grant, dans son processus de dĂ©veloppement, une technique permettant au concepteur du systĂšme de dĂ©limiter la frontiĂšre du SMA et ainsi dĂ©tecter les sources d’erreurs et les contrĂŽler afin que le systĂšme multi-agent soit tolĂ©rant aux fautes (SMATF). Cependant, les mĂ©thodologies de conception de SMA, rĂ©fĂ©rencĂ©es dans la littĂ©rature, n’intĂšgrent pas une telle technique. FATMAS offre au concepteur du SMATF quatre modĂšles pour dĂ©crire et dĂ©velopper le SMA ainsi qu’une technique de rĂ©organisation du systĂšme qui lui permet de dĂ©tecter et de contrĂŽler ses sources d’erreurs, et ainsi dĂ©finir la frontiĂšre du SMA. Chaque modĂšle est associĂ© Ă  un micro processus qui guide le concepteur lors du dĂ©veloppement du modĂšle. FATMAS offre aussi un macro-processus, qui dĂ©finit le cycle de dĂ©veloppement de la mĂ©thodologie. FATMAS se base sur un dĂ©veloppement itĂ©ratif pour identifier et dĂ©terminer les tĂąches Ă  ajouter au systĂšme afin de contrĂŽler des sources d’erreurs. À chaque itĂ©ration, le concepteur Ă©value, selon une fonction de coĂ»t/bĂ©nĂ©fice s’il est opportun d’ajouter de nouvelles tĂąches de contrĂŽle au systĂšme. Le premier modĂšle est le modĂšle de tĂąches-environnement. Il est dĂ©veloppĂ© lors de la phase d’analyse. Il identifie les diffĂ©rentes tĂąches que les agents doivent exĂ©cuter, leurs prĂ©conditions et leurs ressources. Ce modĂšle permet d’identifier diffĂ©rentes sources de problĂšmes qui peuvent causer un disfonctionnement du systĂšme. Le deuxiĂšme modĂšle est le modĂšle d’agents. Il est dĂ©veloppĂ© lors de la phase de conception. Il dĂ©crit les agents, leurs relations, et spĂ©cifie pour chaque agent les ressources auxquelles il a le droit d’accĂ©der. Chaque agent exĂ©cutera un ensemble de tĂąches identifiĂ©es dans le modĂšle de tĂąches-environnement. Le troisiĂšme modĂšle est le modĂšle d’interaction d’agents. Il est dĂ©veloppĂ© lors de la phase de conception. Il dĂ©crit les Ă©changes de messages entre les agents. Le quatriĂšme modĂšle est le modĂšle d’implĂ©mentation. Il est dĂ©veloppĂ© lors de la phase d’implĂ©mentation. Il dĂ©crit l’infrastructure matĂ©rielle sur laquelle le SMA va opĂ©rer ainsi que l’environnement de dĂ©veloppement du SMA. La mĂ©thodologie inclut aussi une technique de rĂ©organisation. Cette technique permet de dĂ©limiter la frontiĂšre du SMA et contrĂŽler, si possible, ses sources d’erreurs. Cette technique doit intĂ©grer trois techniques nĂ©cessaires Ă  la conception d’un systĂšme tolĂ©rant aux fautes : une technique de prĂ©vention d’erreurs, une technique de recouvrement d’erreurs, et une technique de tolĂ©rance aux fautes. La technique de prĂ©vention d’erreurs permet de dĂ©limiter la frontiĂšre du SMA. La technique de recouvrement d’erreurs permet de proposer une architecture du SMA pour dĂ©tecter les erreurs. La technique de tolĂ©rance aux fautes permet de dĂ©finir une procĂ©dure de rĂ©plication d’agents et de tĂąches dans le SMA pour que le SMA soit tolĂ©rant aux fautes. Cette derniĂšre technique, Ă  l’inverse des techniques de tolĂ©rance aux fautes existantes, rĂ©plique les tĂąches et les agents et non seulement les agents. Elle permet ainsi de rĂ©duire la complexitĂ© du systĂšme en diminuant le nombre d’agents Ă  rĂ©pliquer. RĂ©sumĂ© iv De mĂȘme, un agent peut ne pas ĂȘtre en erreur mais la composante matĂ©rielle sur laquelle il est exĂ©cutĂ© peut ne plus ĂȘtre fonctionnelle. Ce qui constitue une source d’erreurs pour le SMA. Il faudrait alors que le SMA continue Ă  s’exĂ©cuter correctement malgrĂš le disfonctionnement d’une composante. FATMAS fournit alors un support au concepteur du systĂšme pour tenir compte de ce type d’erreurs soit en contrĂŽlant les composantes matĂ©rielles, soit en proposant une distribution possible des agents sur les composantes matĂ©rielles disponibles pour que le disfonctionnement d’une composante matĂ©rielle n’affecte pas le fonctionnement du SMA. FATMAS permet d’identifier des sources d’erreurs lors de la phase de conception du systĂšme. Cependant, elle ne traite pas des sources d’erreurs de programmation. Ainsi, la technique de rĂ©organization proposĂ©e dans ce travail sera validĂ©e par rapport aux sources d’erreurs identifiĂ©es lors de la phase de conception et provenant de la frontiĂšre du SMA. Nous dĂ©montrerons formellement que, si une erreur provient d’une composante que le SMA contrĂŽle, le SMA devrait ĂȘtre opĂ©rationnel. Cependant, FATMAS ne certifie pas que le futur systĂšme sera toujours opĂ©rationnel car elle ne traĂźte pas des erreurs de programmation ou des erreurs causĂ©es par son environnement.A multi-agent system (MAS) consists of several agents interacting together. In a MAS, each agent performs several tasks. However, each agent is prone to individual failures so that it can no longer perform its tasks. This can lead the MAS to a failure. Ideally, the MAS should be able to identify the possible sources of failures and try to overcome them in order to continue operating correctly ; we say that it should be fault-tolerant. There are two kinds of sources of failures to an agent : errors originating from the environment with which the agents interacts, and programming exceptions. There are several works on fault-tolerant systems which deals with programming exceptions. However, these techniques does not allow the MAS to identify errors originating from an agent’s environment. In this thesis, we propose a design methodology, called FATMAS, which allows a MAS designer to identify errors originating from agents’ environments. Doing so, the designer can determine the sources of failures it could be able to control and those it could not. Hence, it can determine the errors it can prevent and those it cannot. Consequently, this allows the designer to determine the system’s boundary from its environment. The system boundary is the area within which the decision-taking process of the MAS has power to make things happen, or prevent them from happening.We distinguish between the system’s environment and an agent’s environment. An agent’s environment is characterized by the components (hardware or software) that the agent does not control. However, the system may control some of the agent’s environment components. Consequently, some of the agent’s environment components may not be a part of the system’s environment. The development of a fault-tolerant MAS (FTMAS) requires the use of a methodology to design FTMAS and of a reorganization technique that will allow the MAS designer to identify and control, if possible, different sources of system failure. However, current MAS design methodologies do not integrate such a technique. FATMAS provides four models used to design and implement the target system and a reorganization technique to assist the designer in identifying and controlling different sources of system’s failures. FATMAS also provides a macro process which covers the entire life cycle of the system development as well as several micro processes that guide the designer when developing each model. The macro-process is based on an iterative approach based on a cost/benefit evaluation to help the designer to decide whether to go from one iteration to another. The methodology has three phases : analysis, design, and implementation. The analysis phase develops the task-environment model. This model identifies the different tasks the agents will perform, their resources, and their preconditions. It identifies several possible sources of system failures. The design phase develops the agent model and the agent interaction model. The agent model describes the agents and their resources. Each agent performs several tasks identified in the task-environment model. The agent interaction model describes the messages exchange between agents. The implementation phase develops the implementation model, and allows an automatic code generation of Java agents. The implementation model describes the infrastructure upon which the MAS will operate and the development environment to be used when developing the MAS. The reorganization technique includes three techniques required to design a fault-tolerant system : a fault-prevention technique, a fault-recovery technique, and a fault-tolerance technique. The fault-prevention technique assists the designer in delimiting the system’s boundary. The fault-recovery technique proposes a MAS architecture allowing it to detect failures. The fault-tolerance technique is based on agent and task redundancy. Contrary to existing fault-tolerance techniques, this technique replicates tasks and agents and not only agents. Thus, it minimizes the system complexity by minimizing the number of agents operating in the system. Furthermore, FATMAS helps the designer to deal with possible physical component failures, on which the MAS will operate. It proposes a way to either control these components or to distribute the agents on these components in such a way that if a component is in failure, then the MAS could continue operating properly. The FATMAS methodology presented in this dissertation assists a designer, in its development process, to build fault-tolerant systems. It has the following main contributions : 1. it allows to identify different sources of system failure ; 2. it proposes to introduce new tasks in a MAS to control the identified sources of failures ; 3. it proposes a mechanism which automatically determines which tasks (agents) should be replicated and in which other agents ; 4. it reduces the system complexity by minimizing the replication of agents ; Abstract vii 5. it proposes a MAS reorganization technique which is embedded within the designed MAS and assists the designer to determine the system’s boundary. It proposes a MAS architecture to detect and recover from failures originating from the system boundary. Moreover, it proposes a way to distribute agents on the physical components so that the MAS could continue operating properly in case of a component failure. This could make the MAS more robust to fault prone environments. FATMAS alows to determine different sources of failures of a MAS. The MAS controls the sources of failures situated in its boundary. It does not control the sources of failures situated in its environments. Consequently, the reorganization technique proposed in this dissertation will be proven valid only in the case where the sources of failures are controlled by the MAS. However, it cannot be proven that the future system is fault-tolerant since faults originating from the environment or from coding are not dealt with

    Exploiting the Use of Cooperation in Self-Organizing Reliable Multiagent Systems

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    In this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach

    A Model of Emotion as Patterned Metacontrol

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    Adaptive agents use feedback as a key strategy to cope with un- certainty and change in their environments. The information fed back from the sensorimotor loop into the control subsystem can be used to change four different elements of the controller: parameters associated to the control model, the control model itself, the functional organization of the agent and the functional realization of the agent. There are many change alternatives and hence the complexity of the agent’s space of potential configurations is daunting. The only viable alternative for space- and time-constrained agents —in practical, economical, evolutionary terms— is to achieve a reduction of the dimensionality of this configuration space. Emotions play a critical role in this reduction. The reduction is achieved by func- tionalization, interface minimization and by patterning, i.e. by selection among a predefined set of organizational configurations. This analysis lets us state how autonomy emerges from the integration of cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. Emotion-based morphofunctional systems are able to exhibit complex adaptation patterns at a reduced cognitive cost. In this article we show a general model of how emotion supports functional adaptation and how the emotional biological systems operate following this theoretical model. We will also show how this model is also of applicability to the construction of a wide spectrum of artificial systems1

    The challenge of advanced model-based fdir techniques for aerospace systems: the 2011 situation

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    For aerospace systems, advanced model-based Fault Detection, Identification, and Recovery (FDIR) challenges range from predesign and design stages for upcoming and new programs up to the improvement of the performance of in-service flying systems. However, today, their application to real aerospace world has remained extremely limited. The paper underlines the reasons for a widening gap between the advanced scientific FDIR methods being developed by the academic community and technological solutions demanded by the aerospace industry

    LDM: Lineage-Aware Data Management in Multi-tier Storage Systems

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    We design and develop LDM, a novel data management solution to cater the needs of applications exhibiting the lineage property, i.e. in which the current writes are future reads. In such a class of applications, slow writes significantly hurt the over-all performance of jobs, i.e. current writes determine the fate of next reads. We believe that in a large scale shared production cluster, the issues associated due to data management can be mitigated at a way higher layer in the hierarchy of the I/O path, even before requests to data access are made. Contrary to the current solutions to data management which are mostly reactive and/or based on heuristics, LDM is both deterministic and pro-active. We develop block-graphs, which enable LDM to capture the complete time-based data-task dependency associations, therefore use it to perform life-cycle management through tiering of data blocks. LDM amalgamates the information from the entire data center ecosystem, right from the application code, to file system mappings, the compute and storage devices topology, etc. to make oracle-like deterministic data management decisions. With trace-driven experiments, LDM is able to achieve 29–52% reduction in over-all data center workload execution time. Moreover, by deploying LDM with extensive pre-processing creates efficient data consumption pipelines, which also reduces write and read delays significantly

    Design of Intelligent and Open Avionics System Onboard

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    The continuous development of space missions has put forward requirements for high performance, high reliability, intelligence, effective integration, miniaturization, and quick turn around productization of the electronic system of satellites. The complexity of satellites has continued to increase, and the focus of satellite competition has shifted from the launch of success shifts to communication capacity, performance indicators, degree of flexibility, and continuous service capabilities. So, the importance of onboard avionics system is becoming increasingly prominent. In the future, the advanced avionics system integrates most of the platform’s electronic equipment. The design level of the system largely determines the performance of the satellite platform. This chapter focuses on the application requirements of the new generation of intelligent avionics system for future communication satellites and adopts an “open” architecture of “centralized management, distributed measurement and drive, and software and hardware ‘modular’ design” to build a universal, standardized, and scalable intelligent avionics system

    Blockchain based Decentralized Applications: Technology Review and Development Guidelines

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    Blockchain or Distributed Ledger Technology is a disruptive technology that provides the infrastructure for developing decentralized applications enabling the implementation of novel business models even in traditionally centralized domains. In the last years it has drawn high interest from the academic community, technology developers and startups thus lots of solutions have been developed to address blockchain technology limitations and the requirements of applications software engineering. In this paper, we provide a comprehensive overview of DLT solutions analyzing the addressed challenges, provided solutions and their usage for developing decentralized applications. Our study reviews over 100 blockchain papers and startup initiatives from which we construct a 3-tier based architecture for decentralized applications and we use it to systematically classify the technology solutions. Protocol and Network Tier solutions address the digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scaling Tier solutions address the scalability problems in terms of storage size, transaction throughput, and computational capability. Finally, Federated Tier aggregates integrative solutions across multiple blockchain applications deployments. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design of traditional systems and implementing decentralized applications.Comment: 30 pages, 8 figures, 9 tables, 121 reference

    Formation Flight in Dense Environments

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    Formation flight has a vast potential for aerial robot swarms in various applications. However, existing methods lack the capability to achieve fully autonomous large-scale formation flight in dense environments. To bridge the gap, we present a complete formation flight system that effectively integrates real-world constraints into aerial formation navigation. This paper proposes a differentiable graph-based metric to quantify the overall similarity error between formations. This metric is invariant to rotation, translation, and scaling, providing more freedom for formation coordination. We design a distributed trajectory optimization framework that considers formation similarity, obstacle avoidance, and dynamic feasibility. The optimization is decoupled to make large-scale formation flights computationally feasible. To improve the elasticity of formation navigation in highly constrained scenes, we present a swarm reorganization method which adaptively adjusts the formation parameters and task assignments by generating local navigation goals. A novel swarm agreement strategy called global-remap-local-replan and a formation-level path planner is proposed in this work to coordinate the swarm global planning and local trajectory optimizations efficiently. To validate the proposed method, we design comprehensive benchmarks and simulations with other cutting-edge works in terms of adaptability, predictability, elasticity, resilience, and efficiency. Finally, integrated with palm-sized swarm platforms with onboard computers and sensors, the proposed method demonstrates its efficiency and robustness by achieving the largest scale formation flight in dense outdoor environments.Comment: Submitted for IEEE Transactions on Robotic
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