Organization of Multi-Agent Systems: An Overview

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page

Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments

[EN] This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.This work is partially supported by the TIN2009-13839-C03-01, TIN2011-27652-C03-01, 547CSD2007-00022, COST Action IC0801, FP7-294931 and the FPI grant AP2013-01276 548 awarded to Jaime-Andres Rincon.Rincón Arango, JA.; Poza Luján, JL.; Julian Inglada, VJ.; Posadas Yagüe, JL.; Carrascosa Casamayor, C. (2016). Extending MAM5 Meta-Model and JaCalIVE Framework to Integrate Smart Devices from Real Environments. PLoS ONE. 11(2):1-27. https://doi.org/10.1371/journal.pone.0149665S127112Luck, M., & Aylett, R. (2000). Applying artificial intelligence to virtual reality: Intelligent virtual environments. Applied Artificial Intelligence, 14(1), 3-32. doi:10.1080/088395100117142Barella A, Ricci A, Boissier O, Carrascosa C. MAM5: Multi-Agent Model For Intelligent Virtual Environments. 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Advances in infrastructures and tools for multiagent systems

In the last few years, information system technologies have focused on solving challenges in order to develop distributed applications. Distributed systems can be viewed as collections of service-provider and ser vice-consumer components interlinked by dynamically defined workflows (Luck and McBurney 2008).Alberola Oltra, JM.; Botti Navarro, VJ.; Such Aparicio, JM. (2014). Advances in infrastructures and tools for multiagent systems. Information Systems Frontiers. 16:163-167. doi:10.1007/s10796-014-9493-6S16316716Alberola, J. M., Búrdalo, L., Julián, V., Terrasa, A., & García-Fornes, A. (2014). An adaptive framework for monitoring agent organizations. Information Systems Frontiers, 16(2). doi: 10.1007/s10796-013-9478-x .Alfonso, B., Botti, V., Garrido, A., & Giret, A. (2014). A MAS-based infrastructure for negotiation and its application to a water-right market. 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Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible