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Modèle conceptuel uniforme pour l’adaptation des agents logiciels en environnement ambiant
Abstract
Ce travail s'intéresse à l'adaptation des systèmes informatiques opérant dans un environnement ambiant. Ce type de système est caractérisé par un ensemble de composants hétérogènes, distribués et connectés en réseaux. Ce cadre applicatif ambiant est fortement dynamique. À tout moment, des événements imprévisibles représentent une perturbation potentielle. Par conséquent, d'un côté, la cohésion de l'ensemble des activités collectives n'est pas toujours garantie. D'un autre côté, le fonctionnement normal n'est pas toujours maintenu lorsque les perturbations se présentent. Face à ces problèmes, nous proposons un modèle conceptuel d'architecture pour l'adaptation de ces systèmes informatiques. Notre objectif est d'optimiser l'activité collective. Notre contribution principale est un pattern de conception d'architecture uniforme et générique appelé GMAS. Ce modèle est basé sur une approche orientée comportement. Sa particularité est la vérification de l'applicabilité d'un comportement avant son exécution. Les critères de vérification sont principalement l'état courant du cadre applicatif et celui des interfaces d'action. L'adaptation vient ensuite par ajustement des comportements futurs en fonction des retours de la vérification. Le modèle d'architecture GMAS gère de manière indépendante les événements exceptionnels. Ce qui fait que les objectifs propres à l'agent ou au collectif n'ont pas à tenir compte de ces événements exceptionnels dans leur code source. La gestion des problèmes d'adaptation au niveau individuel et au niveau collectif est uniforme. Nous estimons qu'une adaptation collective passe par la prise en considération de l'individualité. Le but est d'avoir des agents flexibles une fois au niveau collectif. Pour cela, au niveau des composants individuels, l'instance de ce modèle constitue un modèle d'architecture interne. Au niveau de l'ensemble des composants, qui est le niveau global, GMAS offre un modèle de coordination de comportements collectifs basé sur la distribution des modalités opératoires. Avec ces trois propositions de modèle conceptuel : générique, pour le niveau local et pour le niveau global, nous évitons les situations néfastes lors de l'exécution. Nous implémentons chaque modèle sous forme de librairie Java pour montrer son efficacité.
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Self-adaptive software is capable of evaluating and changing its own behavior, whenever the evaluation shows that the software is not accomplishing what it was intended to do, or when better functionality or performance may be possible. The topic of system adaptivity has been widely studied since the mid-60s and, over the past decade, several application areas and technologies relating to self-adaptivity have assumed greater importance. In all these initiatives, software has become the common element that introduces self-adaptability. Thus, the investigation of systematic software engineering approaches is necessary, in order to develop self-adaptive systems that may ideally be applied across multiple domains. The main goal of this study is to review recent progress on self-adaptivity from the standpoint of computer sciences and cybernetics, based on the analysis of state-of-the-art approaches reported in the literature. This review provides an over-arching, integrated view of computer science and software engineering foundations. Moreover, various methods and techniques currently applied in the design of self-adaptive systems are analyzed, as well as some European research initiatives and projects. Finally, the main bottlenecks for the effective application of self-adaptive technology, as well as a set of key research issues on this topic, are precisely identified, in order to overcome current constraints on the effective application of self-adaptivity in its emerging areas of application.
An adaptive system is currently on spot: collective adaptive system (CAS), which is inspired by the socio-technical systems. CASs are characterized by a high degree of adaptation, giving them resilience in the face of perturbations. In CASs, highest degree of adaptation is self-adaptation. The overarching goal of CAS is to realize systems that are tightly entangled with humans and social structures. Meeting this grand challenge of CASs requires a fundamental approach to the notion of self-adaptation. To this end, taking advantage of the categorical approach we establish, in this paper, a firm formal basis for modeling self-adaptation in CASs.
L'environnement, en tant qu'espace partagé entre agents, est un élément essentiel des systèmes multiagents. Selon les systèmes, cet espace intègre des dimensions différentes comme une dimension physique support à l'ancrage spatial et à l'activité des agents sur cette dimension, ou une dimension sociale support aux communications entre agents. Ces dimensions sont souvent traitées de manière indépendante et ne sont reliées qu'au sein de l'agent qui constitue alors le lieu de jonction et de combinaison des informations véhiculées dans ces différentes dimensions. Il s'avère cependant que la combinaison entre ces dimensions est à considérer également en dehors des agents, pour pouvoir par exemple, situer des communications. Dans cet article, nous proposons un modèle unifié supportant la combinaison des dimensions physiques et sociales pour la mise en oeuvre d'interactions contextualisées entre agents. Ce modèle est développé avec le langage multiagent SARL. Nous illustrons cette proposition par une application de simulation de trafic routier dans la ville de Belfort.
The multi-agent systems are successfully used in modeling of dynamic complex systems, and simulations of these models reinforce the knowledge of experts and even allow them to explore new horizons or to cross boundaries. This is the reason why the models being tackled are increasingly varied, and as one goes along with experimentations, these models are completed, intercrossed. Consequently they become increasingly complex. In our previous work [1], we proposed a first modeling approach to support this complexity increase: the Dynamic- Oriented Modeling (DOM). The application of this approach can effectively support the increase of the model. This increase applies to both agents and environments. This DOM approach tackles the problem of the latter by splitting in multiple parts. But if DOM led to organize properly the multiple environments that come into play, little support is provided to organize and manage the increasing complexity of the agents themselves... Inevitably, when we reach a quite advanced stage of evolution of the model, the agents eventually reach a critical mass (either in formalization or code) that makes them more and more hard to comprehend. In this paper, we illustrate this phenomenon and show that it quickly takes the upper hand against the benefits of DOM, as it can eventually block the potential development, or even reuse, of the model. Then we explain that a solution to this ”side effect” could structure the architecture of agents, a structure capable of maintaining readability and flexibility of the formalization of the agent throughout the growth process of the global model.We study a well known pattern in software engineering: the MVC pattern, which can be reused here to meet this objective. We will present in details how this pattern could be instantiated in the field of MAS architecture, and how, ultimately, it can be an effective new way to formalize agents in a method called Multi- Behaviors Modelization.
This article presents a new control architecture designed for heterogeneous modular, multi-configurable, chained micro-robots. This architecture attempts to fill the gap that exists in heterogeneous modular robotics research, in which little work has been conducted compared to that in homogeneous modular robotics studies. The architecture proposes a three-layer structure with a behaviour-based, low-level embedded layer, a half-deliberative half-behaviour-based high layer for the central control, and a heterogeneous middle layer acting as a bridge between these two layers. This middle layer is very important because it allows the central control to treat all modules in the same manner, facilitating the control of the robot. A communication protocol and a module description language were also developed for the control architecture to facilitate communication and information flow between the heterogeneous modules and the central control. Owing to the heterogeneous behaviour of the architecture, the system can automatically reconfigure its actions to adapt to unpredicted events (such as actuator failure). Several behaviours (at low and high levels) are also presented here.
Résumé Récemment, les aspects sociaux et organisation-nels sont devenus des sujets de recherche ma-jeurs en systèmes multi-agents (SMA). Les tra-vaux conduits peuvent être déclinés suivant un point de vue centré agent (ACPV) et un point de vue centré organisation (OCPV), dans les-quels la notion centrale de dynamique des orga-nisations est considérée. Dans ACPV, cette no-tion correspond à des phénomènes ascendants et émergents qui sont regroupés sous le terme général d'auto-organisation. Dans OCPV, cette notion d'organisation nourrit un large panel de travaux relatifs à la réorganisation formelle et descendante d'organisations préexistantes ins-tallées dans le SMA. Dans cet article, nous pro-posons de positionner ces approches afin de construire une vision synthétique de la dyna-mique des organisations dans les SMA. Abstract In the last years, social and organisational as-pects of agency have become a major issue in multi-agent systems' research. The conduc-ted works may be structured along two main points of view : an agent centred point of view (ACPV) and an organisation centred point of view (OCPV). In both approaches the central notion of multi-agent organisation dynamic is considered. In ACPV, this notion leads to a kind of informal, bottom-up, emergent phenomena that we regroup under the general term of self-organisation. In OCPV, this notion gives birth to a huge set of works related to the reorganisation of the formal, top-down, pre-existent organisa-tions that are installed in the MAS. In this pa-per, we propose to position these two approaches to build a comprehensive picture of organisation dynamic in multi-agent systems.
The newest surveillance applications is attempting more complex tasks such as the analysis of the behavior of individuals
and crowds. These complex tasks may use a distributed visual sensor network in order to gain coverage and exploit the inherent
redundancy of the overlapped field of views. This article, presents a Multi-agent architecture based on the Belief-Desire-Intention
(BDI) model for processing the information and fusing the data in a distributed visual sensor network. Instead of exchanging
raw images between the agents involved in the visual network, local signal processing is performed and only the key observed
features are shared. After a registration or calibration phase, the proposed architecture performs tracking, data fusion and
coordination. Using the proposed Multi-agent architecture, we focus on the means of fusing the estimated positions on the
ground plane from different agents which are applied to the same object. This fusion process is used for two different purposes:
(1) to obtain a continuity in the tracking along the field of view of the cameras involved in the distributed network, (2)
to improve the quality of the tracking by means of data fusion techniques, and by discarding non reliable sensors. Experimental
results on two different scenarios show that the designed architecture can successfully track an object even when occlusions
or sensor’s errors take place. The sensor’s errors are reduced by exploiting the inherent redundancy of a visual sensor network
with overlapped field of views.
KeywordsDistributed visual sensor networks–Multi-agent Systems–Data fusion
This article introduces a formal model to specify, model and validate
hierarchical complex systems described at different levels of analysis. It
relies on concepts that have been developed in the multi-agent-based simulation
(MABS) literature: level, influence and reaction. One application of such model
is the specification of hierarchical complex systems, in which decisional
capacities are dynamically adapted at each level with respect to the
emergences/constraints paradigm. In the conclusion, we discuss the main
perspective of this work: the definition of a generic meta-model for holonic
multi-agent systems (HMAS).
Task assignment in multi-agent systems is a complex coordination problem, in particular in systems that are subject to dynamic and changing operating conditions. To enable agents to deal with dynamism and change, adaptive task assignment approaches are needed. In this paper, we study two approaches for adaptive task assignment that are characteristic for two classical families of task assignment approaches. FiTA is a field-based approach in which tasks emit fields in the environment that guide idle agents to tasks. DynCNET is a protocol-based approach that extends standard contract net (CNET). In DynCNET, agents use explicit negotiation to assign tasks. We compare both approaches in a simulation of an industrial automated transportation system. Our experiences show that: (1) the performance of DynCNET and FiTA are similar, while both outperform CNET; (2) the complexity to engineer DynCNET is similar to FiTA but much more complex than CNET; (3) whereas task assignment with FiTA is an emergent solution, DynCNET specifies the interaction among agents explicitly allowing engineers to reason on the assignment of tasks, (4) FiTA is inherently robust to message loss while DynC-NET requires substantial additional support. The tradeoff between (3) and (4) is an important criteria for the selection of an adaptive task assignment approach in practice.
The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems. We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for envi- ronment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.
Multi-Agent Systems (MAS) design methodologies and Integrated Development Environments exhibit many interesting properties
that also support simulation design. Yet, in their current form, they are not appropriate enough to model Multi-Agent Based
Simulations (MABS). Indeed, their design is focused on the functionalities to be achieved by the MAS and the allocation of
these functionalities among software agents. In that context, the most important point of design is the organization of the
agents and how they communicate with each other. On the opposite, MABS aim at studying emergent phenomena, the origin of which
lies in the interactions between entities and their interaction with the environment. In that context, the interactions are
not limited to exchanging messages but can also be fundamental physical interactions or any other actions involving simultaneously
the environment and one or several agents. To deal with this issue, this paper presents the core notions of the Interaction-Oriented Design of Agent simulations (IODA) approach to simulation design. It includes a design methodology, a model, an architecture and also JEDI, a simple implementation of IODA concepts for reactive agents. First of all, our approach focuses on the design of an agent-independent specification of behaviors,
called interactions. These interactions are not limited to the analysis phase of simulation: they are made concrete both in
the model and at the implementation stage. In addition, no distinction is made between agents and objects: all entities of
the simulation are agents. Owing to this principle, designing which interactions occur between agents, as well as how agents
act, is achieved by means of an intuitive plug-and-play process, where interaction abilities are distributed among the agents.
Besides, the guidelines provided by IODA are not limited to the specification of the model as they help the designer from the very beginning towards a concrete implementation
of the simulation.
Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-* properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.
In this work we present a BDI agent architecture to provide mobile soccer robots a high level reasoning mechanism. This architecture is build on top of layered system, where each layer is associated with a di erent level of abstraction in terms of robotic speci cation. The proposed approach allows the declarative speci cation of goal driven robot behavior. However in order to cope with the dynamics of mobile robotics the reasoning mechanism allows reactivity when needed. Syntax, semantics and interactions of the proposed architecture mental components are de ned and studied.
The coordination mechanism is an important aspect of the behaviour based approach. Many strategies are developed to select an action among the proposals of each behaviour. Distributed preference voting present many advantages compared to the other strategies. However, several disadvantages persist. Thus we propose an improvement of this method. The modifications aim particularly at reducing the development problems (choice of votes weights). We propose to select the decisions in a continuous action space. We also increase the number of behaviours that is often used. And finally we try to specify the voting mechanism.
A 2001 IBM manifesto observed that a looming software complexity crisis -caused by applications and environments that number into the tens of millions of lines of code - threatened to halt progress in computing. The manifesto noted the almost impossible difficulty of managing current and planned computing systems, which require integrating several heterogeneous environments into corporate-wide computing systems that extend into the Internet. Autonomic computing, perhaps the most attractive approach to solving this problem, creates systems that can manage themselves when given high-level objectives from administrators. Systems manage themselves according to an administrator's goals. New components integrate as effortlessly as a new cell establishes itself in the human body. These ideas are not science fiction, but elements of the grand challenge to create self-managing computing systems.
In this work we deal with action selection issues for software agent operating in ambient environment which is highly dynamic. Unpredictable events may occur and inappropriate actions may damage the software and its environment. Adaptation ability is a key requirement for such issues. We use multi-agent paradigm to address them. We propose a regulation function within agent architecture. It is a filter which acts on the stream of behaviour before it becomes or not action. The aim is to cope with environmental changes without the need to predict them precisely at design-time. To this end, we introduce the Influence-Reaction Model into the agent behaviour management. To facilitate its application, we implement the resulting architecture as a Java library called MECA. We experiment it with an agricultural robot moving through a field.
Les acteurs virtuels autonomes sont des agents évoluant dans des environnements dynamiques et continus. Ils doivent prendre des décisions afin de s'y adapter en temps réel. Il leur faut donc disposer de modèles décisionnels performants. Les modèles décisionnels orientés comportement sont particulièrement adaptés à ces problèmes. Ils sont fondés sur l'hypothèse qu’une décision peut être répartie entre différentes entités (ou comportements). L’une des difficultés de cette approche est de dégager une décision globale à partir des suggestions des comportements. Dans ce cadre, le vote présente de nombreux avantages. Cependant, dans la littérature, il ne permet pas de sélectionner des actions continues et utilise des pondérations qui biaisent la sélection.Notre contribution consiste en une modification de la méthode de coordination par vote. Notre proposition permet de sélectionner les décisions dans un espace continu, d’utiliser des comportements de granularité fine et met en oeuvre une procédure de vote différente. Nous avons appliqué le modèle décisionnel à la navigation réactive autonome en environnement virtuel. Les essais réalisés prouvent l'intérêt du modèle et n'utilisent aucune pondération. Les résultats en environnement statique montrent que le modèle est capable d'atteindre son objectif en respectant des contraintes et en persistant dans ses choix. Les résultats en environnement dynamique décroissent lorsque le nombre d'agents est supérieur à vingt.De par le caractère ouvert du problème traité, ce travail fait l'objet de plusieurs perspectives applicatives (simulation de groupes d'agents) et théoriques (liaison avec un module de niveau d'abstraction supérieur).
Architecture-based design of multi-agent systems puts software architecture at the center of the software development activities. In this chapter, we give an overview of the approach. We start by situating architecture-based design in a software development life cycle and we give an overview of the methods used in the different steps of architecture-based design of multi-agent systems. Next, we zoom in on the different steps in the approach, including requirements elicitation, architectural design, architecture documentation, and evaluation. For each step, we give the necessary background and we introduce the different techniques and methods that are used. We conclude with a brief explanation of how software architecture serves as a blueprint for system development and a summary.
After introducing the main architectural issues concerning Pervasive Systems, this chapter deals with the essential technological aspects involved in their implementation and deployment. Being it focused on Data Management, we shall introduce the main problems and solutions related to Real-Time Data and the usage of Main-memory Database Management Systems. We will quickly review also some typical advanced data management topics that are useful prerequisites for the subsequent chapters: aspects of Big Data management, Linked Open Data, generalities of information integration including multimedia information management, issues related to the mobility of both users and information sources (e. g.: object tracking, dynamic environmental changes, etc.), the usage of social networks (Facebook, Twitter and others) as information sources and, last but not least, aspects related to the dependability of the system mainly focusing on security and privacy.
The material in this chapter will be introductory and will make extensive reference to the existing literature on the different topics
From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.
Java Agent DEvelopment framework (JADE™) is a leading platform for the development of agent-based systems that are complaint
with Foundation for Intelligent Physical Agents specifications. Due to the complexity, concurrency, and dynamic nature of
manufacturing, it has been an important application of agent-based systems. Application of multi-agent concept in simulation
leads to the agent-based simulation. Modeling the elements of manufacturing system (such as part, machine, and AGV) in reactive
agent architecture is a better way of modeling for achieving discrete-event agent-based simulation. This paper focuses on
modeling of different agents in manufacturing domain on JADE reactive architecture. Modeling of different agents on a shop
floor in JADE reactive architecture led to the development of a simulator known as an agent-based shop floor simulator (ABSFSim).
In the modeling process, different agents in the manufacturing domain have been identified by physical and functional decomposition.
Internal architecture of individual agents is finalized based on their behavioral requirements. Modeling of the agents is
an important development step of ABSFSim. A randomly generated sample manufacturing system has been used for testing and demonstration
of ABSFSim. The modeling details provided in this paper are useful for development of agent-based systems in manufacturing
domain as well as other discrete systems.
KeywordsAgent-based modeling–Reactive architecture–FIPA–JADE–AGVS
A new architecture for controlling mobile robots is described. Layers of control system are built to let the robot operate at increasing levels of competence. Layers are made up of asynchronous modules that communicate over low-bandwidth channels. Each module is an instance of a fairly simple computational machine. Higher-level layers can subsume the roles of lower levels by suppressing their outputs. However, lower levels continue to function as higher levels are added. The result is a robust and flexible robot control system. The system has been used to control a mobile robot wandering around unconstrained laboratory areas and computer machine rooms. Eventually it is intended to control a robot that wanders the office areas of our laboratory, building maps of its surroundings using an onboard arm to perform simple tasks.
Reinforcement learning (RL) has received some attention in recent years from agent-based researchers because it deals with the problem of how an autonomous agent can learn to select proper actions for achieving its goals through interacting with its environment. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored yet. In this paper, Q-learning, a popular RL algorithm, is applied to a single machine dispatching rule selection problem. This paper investigates the application potential of Q-learning, a widely used RL algorithm to a dispatching rule selection problem on a single machine to determine if it can be used to enable a single machine agent to learn commonly accepted dispatching rules for three example cases in which the best dispatching rules have been previously defined. This study provided encouraging results that show the potential of RL for application to agent-based production scheduling.
A self-adaptive agent-based fuzzy-neural system is constructed in this study to enhance the performance of scheduling jobs in a wafer fabrication factory. The system integrates dispatching, performance evaluation and reporting, and scheduling policy optimization. Unlike in the past studies a single pre-determined scheduling algorithm is used for all agents, in this study every agent develops and modifies its own scheduling algorithm to adapt it to the local conditions. To stabilize the performance of the self-adaptive agent-based fuzzy-neural scheduling system, some treatments have also been taken. To evaluate the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation is also applied in this study to generate some test data. According to experimental results, the self-adaptive agent-based fuzzy-neural system did improve the performance of scheduling jobs in the simulated wafer fabrication factory, especially with respect to the average cycle time and cycle time standard deviation.
We illustrate the Cooperation through Implicit Communication behavior-based approach used for developing the PaSo-Team (The University of Padua Simulated Soccer Robot Team), a multi-robot software system for soccer robot competitions promoted by the RoboCup Simulation League. The configuration of the environment, namely the robots’ relative positions depending on both the global task and the game dynamics, provides a source of implicit information about the robots’ intention to be involved in collective actions, making them able to cooperate implicitly. The soccer team performance can be tuned by triggering the arbitration module of any single robot to generate, as many as possible, suitable situations which hint to the team the action of scoring the goal. Some macroscopic parameters can be usefully introduced to evaluate the evolution of the whole multi-robot software system.
In behavior-based robotics the control of a robot is shared between a set of purposive perception-action units, called behaviors. Based on selective sensory information, each behavior produces immediate reactions to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot's overall task such as obstacle avoidance or wall following. Behaviors with different and possibly incommensurable objectives may produce conflicting actions that are seemingly irreconcilable. Thus a major issue in the design of behavior-based control systems is the formulation of effective mechanisms for coordination of the behaviors' activities into strategies for rational and coherent behavior. This is known as the action selection problem (also refereed to as the behavior coordination problem) and is the primary focus of this overview paper. Numerous action selection mechanisms have been proposed over the last decade and the main objective of this document is to give a qualitat...
Ce sont : le Mailbox pour l'échange de message, l'Alias pour l'identité, le State pour l'état, le Skill pour les compétences, et l'Activity pour les activités. Ces supports encore appelés "facette" décrivent un agent dans l'espace d'interaction sociale
- Ubiquity
Ubiquity. Nous nous intéressons particulièrement ici aux classes représentant un agent.
La librairie est constituée de plusieurs packages. Nous nous intéressons uniquement ici au
package gérant la représentation de l'agent dans le space.
Pour tirer profit de la plateforme, Ubiquity offre des outils d'interaction aux agents.
Pour cela, un agent est représenté par cinq supports. Ce sont : le Mailbox pour l'échange
de message, l'Alias pour l'identité, le State pour l'état, le Skill pour les compétences, et
l'Activity pour les activités. Ces supports encore appelés "facette" décrivent un agent dans
l'espace d'interaction sociale. Dans notre travail, nous nous intéressons particulièrement aux
comportements. Au lieu d'utiliser ces 5 facettes, nous en utilisons principalement deux : Alias,
et Skill et Mailbox. L' objectif du comportement est décrit dans Skill. Le Mailbox intervient dans
la réception et l'envoi des messages. Il remplit ici le rôle d'interface d'écoute et d'interface
d'envoi également.
Pour cela, la librairie offre deux interfaces spécifiques pour cette interaction : Avatar et Participant. -L'interface Avatar représente l'ensemble des facettes définissant l'agent. Un agent est donc représenté par un et un seul Avatar dans le space
- Dans Ubiquity
Dans Ubiquity, Les agents se voient et interagissent par des représentations particulières.
Pour cela, la librairie offre deux interfaces spécifiques pour cette interaction : Avatar et
Participant.
-L'interface Avatar représente l'ensemble des facettes définissant l'agent. Un agent est
donc représenté par un et un seul Avatar dans le space. Nous avons ici le même image
que comme au sein d'un réseau social où les utilisateurs interagissent en utilisant un
avatar virtuel.
Engineering Self-Organising Systems, Nature-Inspired Approaches to Software Engineering [revised and extended papers presented at the Engineering Self-Organising Applications Workshop, ESOA
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- Marie-Pierre Gleizes
- Gauthier Picard
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Applications Workshop, ESOA 2003, held at AAMAS 2003 in Melbourne, Australia,
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Influences and reaction : a model of situated multiagent systems
- Jacques Ferber
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The MADKIT agent platform architecture
- Olivier Gutknecht
- Jacques Ferber
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Papers, volume 1887 of Lecture Notes in Computer Science, pages 48-55. Springer,
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Systèmes complexes à base de Multi-Agents situés
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Salima Hassas. Systèmes complexes à base de Multi-Agents situés. 2003.
Challenges in development of real time multi-robot system using behaviour based agents
- Aleksis Liekna
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- Agris Nikitenko
Aleksis Liekna, Egons Lavendelis, and Agris Nikitenko. Challenges in development of real time multi-robot system using behaviour based agents. In
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Weak interaction and strong interaction in agent based simulations
- Fabien Michel
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