Federated Robust Embedded Systems: Concepts and Challenges

The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements. While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development. During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs

A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things

The Internet of Things (IoT) is envisioned as a global network of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. However, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT systems and applications, it is key to orchestrate heterogeneous 'things' in a seamless, adaptive and dynamic manner, such that the system can exhibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users' requirements. To this end, contextualisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user's interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users' intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.Comment: In Proceedings of the Second International Workshop on the Internet of Agents @AAMAS201

Emerging communication between competitive agents

Nous utilisons l’apprentissage automatique pour répondre à une question fondamentale: comment les individus peuvent apprendre à communiquer pour partager de l'information et se coordonner même en présence de conflits? Cette th\`ese essaie de corriger l'idée qui prévaut à l'heure actuelle dans la communauté de l'apprentissage profond que les agents compétitifs ne peuvent pas apprendre à communiquer efficacement. Dans ce travail de recherche, nous étudions l’émergence de la communication dans les jeux coopératifs-compétitifs à travers un jeu expéditeur-receveur que nous construisons. Nous portons aussi une attention particulière à la qualité de notre évaluation. Nous observons que les agents peuvent en effet apprendre à communiquer, confirmant des résultats connus dans les domaines des sciences économiques. Nous trouvons également trois façons d'améliorer le protocole de communication appris. Premierement, l'efficacité de la communication est proportionnelle au niveau de coopération entre les agents, les agents apprennent à communiquer plus facilement quand le jeu est plus coopératif que compétitif. Ensuite, LOLA (Foerster et al, 2018) peut améliorer la stabilité de l'entraînement et l'efficacité de la communication, principalement dans les jeux compétitifs. Et enfin, que les protocoles de communication discrets sont plus adaptés à l'apprentissage d'un protocole de communication juste et coopératif que les protocoles de communication continus. Le chapitre 1 présente une introduction aux techniques d'apprentissage utilisées par les agents, l'apprentissage automatique et l'apprentissage par renforcement, ainsi qu'une description des méthodes d'apprentissage par renforcement propre aux systemes multi-agents. Nous présentons ensuite un historique de l'émergence du language dans d'autres domaines tels que la biologie, la théorie des jeux évolutionnaires, et les sciences économiques. Le chapitre 2 approndit le sujet de l'émergence de la communication entre agents compétitifs. Le chapitre 3 présente les conclusions de notre travail et expose les enjeux et défis de l'apprentissage de la communication dans un environment compétitif.We investigate the fundamental question of how agents in competition learn communication protocols in order to share information and coordinate with each other. This work aims to overturn current literature in machine learning which holds that unaligned, self-interested agents do not learn to communicate effectively. To study emergent communication for the spectrum of cooperative-competitive games, we introduce a carefully constructed sender-receiver game and put special care into evaluation. We find that communication can indeed emerge in partially-competitive scenarios, and we discover three things that are tied to improving it. First, that selfish communication is proportional to cooperation, and it naturally occurs for situations that are more cooperative than competitive. Second, that stability and performance are improved by using LOLA (Foerster et al, 2018), a higher order ``theory-of-mind'' learning algorith, especially in more competitive scenarios. And third, that discrete protocols lend themselves better to learning fair, cooperative communication than continuous ones. Chapter 1 provides an introduction to the underlying learning techniques of the agents, Machine Learning and Reinforcement Learning, and provides an overview of approaches to Multi-Agent Reinforcement Learning for different types of games. It then gives a background on language emergence by motivating this study and examining the history of techniques and results across Biology, Evolutionary Game Theory, and Economics. Chapter 2 delves into the work on language emergence between selfish, competitive agents. Chapter 3 draws conclusion from the work and points out the intrigue and challenge of learning communication in a competitive setting, setting the stage for future work

The Future of Computerized Decision Support in Critical Care

book chapterBiomedical Informatic

COIN@AAMAS2015

COIN@AAMAS2015 is the nineteenth edition of the series and the fourteen papers included in these proceedings demonstrate the vitality of the community and will provide the grounds for a solid workshop program and what we expect will be a most enjoyable and enriching debate.Peer reviewe