112 research outputs found

    Meeting the challenges of decentralized embedded applications using multi-agent systems

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    International audienceToday embedded applications become large scale andstrongly constrained. They require a decentralized embedded intelligencegenerating challenges for embedded systems. A multi-agent approach iswell suited to model and design decentralized embedded applications.It is naturally able to take up some of these challenges. But somespecific points have to be introduced, enforced or improved in multiagentapproaches to reach all features and all requirements. In thisarticle, we present a study of specific activities that can complementmulti-agent paradigm in the ”embedded” context.We use our experiencewith the DIAMOND method to introduce and illustrate these featuresand activities

    Artificial Emotions for Distributed Cyber-physical Systems Resilience

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    International audienceThe concept of system resilience is important and popular in different domains like psychology, psychiatry, sociology, and more recently in cognitive science, biological disciplines, ecology and computer science. The main objective of this paper is to present a research avenue exploring the applicability of knowledge from those domains to solve resilience problems in cyber-physical systems. Emotions have been identified as an important process to cope with unexpected events and is therefore crucial for resilience. Our work is thus aimed at utilizing emotion-like processes in cyber-physical systems to improve their resilience, at individual and collective levels. Furthermore, one of our main assumptions is that the multi-agent paradigm is particularly well suited to embed such emotion-like processes in this type of systems

    Engineering Multi-Agent Systems: State of Affairs and the Road Ahead

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    The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area

    Smart Residential Buildings as Learning Agent Organizations in the Internet of Things

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    Background: Smart buildings are one of the major application areas of technologies bound to embedded systems and the Internet of things. Such systems have to be adaptable and flexible in order to provide better services to its residents. Modelling such systems is an open research question. Herein, the question is approached using an organizational modelling methodology bound to the principles of the learning organization. Objectives: Providing a higher level of abstraction for understanding, developing and maintaining smart residential buildings in a more human understandable form. Methods/Approach: Organization theory provides us with the necessary concepts and methodology to approach complex organizational systems. Results: A set of principles for building learning agent organizations, a formalization of learning processes for agents, a framework for modelling knowledge transfer between agents and the environment, and a tailored organizational structure for smart residential buildings based on Nonaka’s hypertext organizational form. Conclusions: Organization theory is a promising field of research when dealing with complex engineering systems

    Démarche, modèles et outils multi-agents pour l'ingénierie des collectifs cyber-physiques

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    We call a Collective Cyber-Physical System (CCPS), a system consisting of numerous autonomous execution units achieving tasks of control, communication, data processing or acquisition. These nodes are autonomous in decision making and they can cooperate to overcome gaps of knowledge or individual skills in goal achievement.There are many challenges in the design of these collective systems. This Habilitation thesis discusses various aspects of such a system engineering modeled according to a multi-agent approach.First, a complete CCPS design method is proposed. Its special features are discussed regarding the challenges mentioned above. Agent models and collective models suitable to constrained communications and changing environments are then proposed to facilitate the design of CCPS. Finally, a tool that enables the simulation and the deployment of hw/sw mixed collective systems is presented.These contributions have been used in several academic and industrial projects whose experience feedbacks are discussed.Nous appelons "collectif cyber-physique" un système embarqué en réseau dans lequel les nœuds ont une autonomie de décision et coopèrent spontanément afin de participer à l'accomplissement d'objectifs du système global ou de pallier des manques de connaissances ou de compétences individuelles. Ces objectifs portent notamment sur l'état de leur environnement physique. La conception de ces collectifs présente de nombreux défis. Ce mémoire d'Habilitation propose une discussion des différents aspects de l'ingénierie de ces systèmes que nous modélisons en utilisant le paradigme multi-agent. Tout d'abord, une méthode complète d'analyse et de conception est proposée. Ses différentes particularités sont discutées au regard des différents défis précédemment évoqués. Des modèles d'agent et de collectifs adaptés aux communications contraintes et aux environnements changeants sont alors proposés. Ils permettent de simplifier la conception des collectifs cyber-physiques. Enfin, un outil qui permet la simulation et le déploiement de systèmes collectifs mixtes logiciels/matériels est introduit.Ces contributions ont été éprouvées dans des projets académiques et industriels dont les retours d'expériences sont exploités dans les différentes discussions

    Conceptual-based reasoning in mobile web 2.0 by means multiagent systems - knowledge engineering notes

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    Increasingly, users connect to the Internet by mobile devices and they are generating massive content through them. The lead-off projects in Mobile Web 2.0 offer the opportunity to add semantics in order to obtain structured knowledge. In this paper, we present specific challenges for tagging reasoning, into the SinNet project. SinNet is based on user generated content (UGC) by mobile devices, as well as how to solve them by means of combining multi-agent systems and formal concepts analysis.Ministerio de Ciencia e Innovación TIN2009-0949

    Proceedings of the 2012 Workshop on Ambient Intelligence Infrastructures (WAmIi)

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    This is a technical report including the papers presented at the Workshop on Ambient Intelligence Infrastructures (WAmIi) that took place in conjunction with the International Joint Conference on Ambient Intelligence (AmI) in Pisa, Italy on November 13, 2012. The motivation for organizing the workshop was the wish to learn from past experience on Ambient Intelligence systems, and in particular, on the lessons learned on the system architecture of such systems. A significant number of European projects and other research have been performed, often with the goal of developing AmI technology to showcase AmI scenarios. We believe that for AmI to become further successfully accepted the system architecture is essential

    Proceedings of the 2012 Workshop on Ambient Intelligence Infrastructures (WAmIi)

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    This is a technical report including the papers presented at the Workshop on Ambient Intelligence Infrastructures (WAmIi) that took place in conjunction with the International Joint Conference on Ambient Intelligence (AmI) in Pisa, Italy on November 13, 2012. The motivation for organizing the workshop was the wish to learn from past experience on Ambient Intelligence systems, and in particular, on the lessons learned on the system architecture of such systems. A significant number of European projects and other research have been performed, often with the goal of developing AmI technology to showcase AmI scenarios. We believe that for AmI to become further successfully accepted the system architecture is essential

    Deep learning based approaches for imitation learning.

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    Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable

    Regulated MAS: Social Perspective

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    This chapter addresses the problem of building normative multi-agent systems in terms of regulatory mechanisms. It describes a static conceptual model through which one can specify normative multi-agent systems along with a dynamic model to capture their operation and evolution. The chapter proposes a typology of applications and presents some open problems. In the last section, the authors express their individual views on these mattersMunindar Singh’s effort was partially supported by the U.S. Army Research Office under grant W911NF-08-1-0105. The content of this paper does not necessarily reflect the position or policy of the U.S. Government; no official endorsement should be inferred or implied. Nicoletta Fornara’s effort is supported by the Hasler Foundation project nr. 11115-KG and by the SER project nr. C08.0114 within the COST Action IC0801 Agreement Technologies. Henrique Lopes Cardoso’s effort is supported by Fundação para a Ciência e a Tecnologia (FCT), under project PTDC/EIA-EIA/104420/2008. Pablo Noriega’s effort has been partially supported by the Spanish Ministry of Science and Technology through the Agreement Technologies CONSOLIDER project under contract CSD2007-0022, and the Generalitat of Catalunya grant 2009-SGR-1434.Peer Reviewe
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