Jean-Paul Arcangeli

INCOME : multi-scale context management for the internet of things

Nowadays, context management solutions in ambient networks are well-known. However, with the IoT paradigm, ambient information is not anymore the only source of context. Context management solutions able to address multiple network scales ranging from ambient networks to the Internet of Things (IoT) are required. We present the INCOME project whose goal is to provide generic software and middleware components to ease the design and development of mass market context-aware applications built above the Internet of Things. By revisiting ambient intelligence (AmI) context management solutions for extending them to the IoT, INCOME allows to bridge the gap between these two very active research domains. In this landscape paper, we identify how INCOME plans to advance the state of the art and we briefly describe its scientific program which consists of three main tasks: (i) multi-scale context management, (ii) management of extrafunctional concerns (quality of context and privacy), and (iii) autonomous deployment of context management entities.

ADELFE Design, AMAS-ML in Action

The complexity of engineers tasks leads us to provide means to bring the Adaptive Multi-Agent Systems (AMAS) design to a higher stage of automation and confidence thanks to Model Driven Development (MDD). This paper focuses on a practical example and illustrates the modifications that have been done to the ADELFE methodology. In the Design phase, we propose to use a Domain Specific Modeling Language (DSML) for the specification of cooperative agents. We also, add a Model Diven Implementation phase using model transformation, DSMLs and code generation. These phases carry out a model centric process to produce and partially generate the system code. We present the use of our MD process applied to a simple, but very illustrative example: the foraging ants simulation.

Development of flexible peer-to-peer information systems using adaptable mobile agents

Wide-area networks provide an easy access to many different distributed and heterogeneous data sources. The development of automated operating tools is still complex, particularly because of evolution and adaptation requirements (to the data sources structures and to the network quality of service). The purpose of this paper is to evaluate the advantages of adaptable mobile agents in order to simplify the development and the deployment. As an experiment, we develop a prototype of peer-to-peer information system, and we show how agent mobility and adaptation abilities help in implementing various forms of adaptation: adaptation to the execution context, access to new servers with initially unknown communication protocols, dynamic modification of search algorithms based on results provided by the servers. Then, we show how these techniques can be easily extended to other problems such as search and upgrade of software components.

The MAELIA Multi-Agent Platform for Integrated Analysis of Interactions Between Agricultural Land-Use and Low-Water Management Strategies

The MAELIA project is developing an agent-based modeling and simulation platform to study the environmental, economic and social impacts of various regulations regarding water use and water management in combination with climate change. It is applied to the case of the French Adour-Garonne Basin, which is the most concerned in France by water scarcity during the low-water period. An integrated approach has been chosen to model this social-ecological system: the model combines spatiotemporal models of ecologic (e.g. rainfall and temperature changes, water flow and plant growth) and socio-economic (e.g. farmer decision-making process, management of low-water flow, demography, land use and land cover changes) processes and sub-models of cognitive sharing among agents (e.g. weather forecast, normative constraints on behaviors)

Adaptive Autonomous Agent Models for Open Distributed Systems

Currently, there is a lack of tools to build autonomous software systems in a pervasive and distributed context. In this paper, we present a model and a development tool called Agentphi for building flexible agent architectures from fine-grained components. Reusable components implement non-functional mechanisms such as communication, mobility or adaptation skills. Each agent can dynamically and autonomously change its components to suit its runtime context, improving safety and performances of applications, particularly in open, pervasive or large-scale distributed applications. We also show how to use our agents to build applications from autonomous and adaptable systems.

MuScADeL: A Deployment DSL Based on a Multiscale Characterization Framework

With the Internet of Things (IoT) paradigm, ambient systems move from locally distributed systems to Internet distributed systems. These systems become huge in term of number of devices and imply high heterogeneity (e.g., Of devices, of networks). They are continuously evolving with appearing and disappearing devices at runtime. The inner complexity of these systems, called multiscale systems, requires autonomic deployment middleware. Such middleware should deploy components where and when necessary, and adapt the architecture of the deployed systems considering the different scales of the systems. In this paper, we define MuScADeL, a domain-specific language (DSL) dedicated to multiscale and autonomic software deployment. MuScADeL allows designers to abstractly define deployment properties without exact knowledge of the devices and networks the system will be deployed on. This DSL is based on a scale-awareness framework, which helps designers to characterize the multiscale nature of a system from several viewpoints such as device, network, administration and geography. With MuScADeL, deployment designers may express multiscale properties of systems to deploy. MuScADeL is a building block for deployment middleware that targets multiscale distributed systems. We illustrate the possibilities of MuScADeL through a smart transport scenario.

MAS organisations to adapt your composite service

Adapting composite web-services is a concern of many researchers from the services community and a requirement of the industry. We propose the CASAS (Composable, Adaptive, Service, Agent System) framework that provides mechanisms to monitor and pro-actively adapt composite services. The framework integrates concepts of Service Oriented Computing and Agent Organisations, offering monitoring and adaptation mechanisms to deal with adaptation in service compositions. CASAS is an improvement on related work in that it offers a high-level model that allows the definition and enforcement of global constraints for the service composition. We explain CASAS in detail and conclude by showing how one can use it to create an adaptable composite service written in the Business Process Execution Language (BPEL).

Engineering Agent Frameworks: An Application in Multi-Robot Systems

In this paper, we present a novel development process called SPEARAF (Species to Engineer Architectures for Agent Frameworks) and evaluate its relevance to ease the implementation of Multi-Agent Systems in the context of a multirobot project for crisis management. SPEARAF allows to build component-based architectures for agents and their infrastructure. We show the advantages of using an architecture-based process to realise an application-specific agent framework adapted to the requirements of such a system. SPEARAF gives guidelines to enables the use of architecture-oriented practices for agent implementation.

Flexible Architectures and Agents for Adaptive Autonomic Systems

In order to simplify the development of autonomic and autonomous systems, we propose a model of adaptive agent built from fine-grained reusable components which implement non-functional mechanisms such as communication, mobility or adaptation skills. Each agent can dynamically and autonomously change its components to fit its runtime context, improving safety and performance in particular for open, pervasive or large-scale distributed applications. We describe a tool called Agent phi for adaptive agent modeling and we present the design of an embedded agent

Opportunistic software composition: Benefits and requirements

Traditional software development relies on building and assembling pieces of software in order to satisfy explicit requirements. Component-based software engineering simplifies composition and reuse, but software adaptation to the environment remains a challenge. Opportunistic composition is a new approach for building and re-building software in open and dynamic contexts. It is based on the ability to compose software components in a bottom-up manner, merely because they are available at a point and not because the construction of a specific software has been demanded. In this way, software emerges from the environment. This paper analyzes the advantages of such an approach in terms of flexibility and reuse, along with the requirements that an infrastructure supporting opportunistic composition should satisfy: it should be decentralized, autonomic, and dynamically adaptive. The state of the art of automatic software composition shows that few solutions are actually bottom-up, and that none of them fully satisfies the requirements of opportunistic composition.