Flexible and Emergent Workflows using Adaptive Agents

International audienceMost of existing workflow systems are rigid since they require to completely specify processes before their enactment and they also lack flexibility during their execution. This work proposes to view a workflow as a set of cooperative and adaptive agents interleaving its design and its execution leading to an emergent workflow. We use the theory of Adaptive Multi-Agent Systems (AMAS) to provide agents with adaptive capabilities and the whole multi-agent system with emergent "feature". We provide a meta-model linking workflow and AMAS concepts, and the specification of agent behavior and the resulting collaborations. A simulator has been implemented with the Make Agent Yourself platform

DREAM: Dynamic data Relation Extraction using Adaptive Multi-agent systems

Understanding data is the main purpose of data science and how to achieve it is one of data science challenges, especially when dealing with big data. In order to find meaning and relevant information drowned in the data flood, while overcoming big data challenges, one should rely on an analytic tool able to find relations between data, evaluate them and detect their changes and evolution over time. The aim of this paper is to present the DREAM1 tool for dynamic data relations discovery and dynamic display based on a collective artificial intelligence Adaptive Multi-Agent System (AMAS) that uses a new data similarity metric, the Dynamics Correlation. It is currently being applied in the neOCampus operation, the ambient campus of the University of Toulouse III - Paul Sabatier

Agent-Based Modeling and Simulation of Biological Systems

Agent-based modeling and simulation is a powerful technique in simulating and exploring phenomena that includes a large set of active components represented by agents. The agents are actors operating in a real system, influencing the simulated environment and influenced by the simulated environment. The agents are included in the simulation model as model components performing actions autonomously and interacting with other agents and the simulated environment to represent behaviors in the real system. In this chapter, we describe how to develop an agent-based model and simulation for biological systems in Repast Simphony platform, which is a Java-based modeling system. Repast Simphony helps developers to create a scenario tree including displays of agents, grid and continuous space, data sets, data loaders, histogram, and time charts. At the end of this chapter, we present case studies developed by our research group with references to demonstrate local behavior of biological system

Deploying self-organisation to improve task execution in a multi-agent systems

This paper discusses how the performance of a network of agents can be improved using a self-organisation technique. The multi-agent network performance can be improved by organizing the agents in clusters. Furthermore, principles of self-organisation can be used to create agent organisations triggered when some of the agents have high load. Hence, busy agents within the network may decide to create an organisation to receive extra support from other less busy agents in order to execute more tasks. The paper presents a simulation based on Repast Simphony that has been used to develop the proposed model and describes a set of experiments showing the performance of the system with and without the self-organisation technique

A Natural Formalism and a MultiAgent Algorithm for Integrative Multidisciplinary Design Optimization

International audienceMultiDisciplinary Optimization (MDO) problems represent one of the hardest and broadest domains of continuous optimization. By involving both the models and criteria of different disciplines, MDO problems are often too complex to be tackled by classical optimization methods. We propose an approach which takes into account this complexity using a new representation (NDMO - Natural Domain Modeling for Optimization) and a self-adaptive multi-agent algorithm. Our method agentifies the different elements of the problem (such as the variables, the models, the objectives). Each agent is in charge of a small part of the problem and cooperates with others to find equilibrium on conflicting values. Despite the fact that no agent of the system has a complete view of the entire problem, the mechanisms we provide allow the emergence of a coherent solution. Evaluations on several academic and industrial test cases are provided

A Reference Architecture for Digital Ecosystems

Digital ecosystems are a new type of application based on a “universal digital environment” populated by digital entities that form communities that evolve and interact with information exchange and who trade digital objects that are produced through the system. Entities that participate and form the ecosystem can be applications running not only on simple devices: wearable, sensors, actuators, but also on complex services executed on smartphones, tablets, personal computers, company servers, etc. A reference architecture for digital ecosystems is a step toward standardization, as it defines a set of guidelines in designing and implementing a digital ecosystem. Often such architectures are very abstract, difficult to understand and implement. In this chapter, we introduce a vendor- and technology-neutral reference architecture for digital ecosystems and apply this architecture to an actual use case

Holarchical Innovation Teams: Terms & Definitions

This paper establishes the terms and definitions for the nascent discipline of Holarchical Innovation Teams (HITs). It provides a Review of Literature of those individuals who have contributed to our understanding of holarchies and who assist in creating an etymology for HITs in order to lay the foundations for subsequent papers on HITs philosophy and principles for future researchers and scholars of the disciplin

Interoperability based Dynamic Data Mediation using Adaptive Multi-Agent Systems for Co-Simulation

A co-simulation is the coupling of several simulation tools where each one handles part of a modular problem which allows each designer to interact with the complex system in order to retain its business expertise and continue to use its own digital tools. For this co-simulation to work, the ability to exchange data between the tools in meaningful ways, known as Interoperability, is required. This paper describes the design of such interoperability based on the FMI (Functional Mock up Interface) standard and a dynamic data mediation using adaptive multi-agent systems for a co-simulation. It is currently being applied in neOCampus, the ambient campus of the University of Toulouse III - Paul Sabatier