114,087 research outputs found

    Self-Organizing Multi-Agent Systems for the Control of Complex Systems

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    Because of the law of requisite variety, designing a controller for complex systems implies designing a complex system. In software engineering, usual top-down approaches become inadequate to design such systems. The Adaptive Multi-Agent Systems (AMAS) approach relies on the cooperative self-organization of autonomous micro-level agents to tackle macro-level complexity. This bottom-up approach provides adaptive, scalable, and robust systems. This paper presents a complex system controller that has been designed following this approach, and shows results obtained with the automatic tuning of a real internal combustion engine

    Modeling and analysis of a simple manufacturing-oriented multi-agent system

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    Due to the autonomy of individual agents and the use of the concept of distributed planning, multi-agent systems (MAS) represent a promising approach to achieve fault-tolerant self-organizing manufacturing systems. In this article, a basic component of a manufacturing-oriented MAS is presented. The negotiation strategies are formulated in such a way that they, on the one hand, guarantee considerable flexibility of the basic component itself, and, on the other hand, enable the construction of more complex systems built up from several components. On the basis of this single component, it is shown that the dynamics of such systems without appropriate control mechanisms can be chaotic. Such behaviour is, however, unwanted in practice and must therefore be stabilized or avoided. In order to develop appropriate tools for this task, the dynamic behaviour of the system is investigated using concepts and methods of synergetics and the theory of nonlinear dynamical systems

    Challenges for adaptation in agent societies

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    The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. 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In: Coordination support international joint conference on autonomous agents and multiagent systems (AAMAS), pp 1301–1302Campos J, Esteva M, López-Sánchez M, Morales J, Salamó M (2011) Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing 91(2):169–215Carley KM, and Gasser L (1999) Computational organization theory. Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, pp 299–330Carvalho G, Almeida H, Gatti M, Vinicius G, Paes R, Perkusich, A, Lucena C (2006) Dynamic law evolution in governance mechanisms for open multi-agent systems. In: Second workshop on software engineering for agent-oriented systemsCernuzzi L, Zambonelli F (2011) Adaptive organizational changes in agent-oriented methodologies. 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    A design space exploration method for Identifying emergent behavior in complex systems

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    This research seeks to gain insight into the design of distributed multi-agent systems. Distributed multi-agent systems present opportunities for accomplishing a goal using multiple simple systems rather than a more complicated monolithic system. Distributed systems, if properly designed, have the potential to exhibit self-organizing behavior which can lead to systems that require less centralized control in addition to improved robustness, reliability, scalability, and adaptability than traditional monolithic, centralized systems. As engineered systems become more complex, their behavior is more difficult to characterize and predict. Self-organizing systems are difficult to analyze and design since the system behavior is emergent, i.e., the collective behavior only becomes apparent once the system is integrated. The collective behavior is primarily driven by the local interactions of the agents and their environment. This poses an enormous challenge for engineering these systems. The task of system design---selecting the right rules and system parameters---is difficult due to the opaque connection between inputs and responses. The goal of this research is to develop a methodology that provides a way of systematically exploring the design space in order to identify the conditions that give rise to emergent behavior. This information can be used as part of the scientific process of providing feedback through the iterative design process. In order to address this goal, this research seeks to answer the question on how to define, measure, and use the concept of emergence in the design of a multi-agent system. Similarly, it will address the more general question about how to understand "complex systems" in order to analyze and engineer them. This will be used to guide the development of an appropriate methodology. This research develops the Systematic Exploration for Emergence Detection (SEED) methodology for evaluating computer simulations of complex systems in order to identify conditions that lead to emergent behavior. This research proposes a new quantitative measure of emergence which can identify critical transitions in macro-level performance/function of the system due to changes in system context (i.e., environmental conditions or system parameters). The methodology provides the framework for performing a design space exploration using this measure of emergence to identify critical regions in the design space. These regions help to characterize the design space and will help guide the design process by providing insight into design points where the system behavior is unexpected or changing rapidly, which are possible indicators of emergent behavior. The SEED methodology is based on a statistical analysis approach. The design space is efficiently sampled using Design of Experiments methods. At each of these design points, the system behavior is characterized statistically using repeated runs of the simulation. The proposed measure of emergence, Design Space Divergence, is then evaluated across the design space and critical regions are identified using data visualization and clustering methods. A case study is performed on a multi-UAV distributed surveillance problem to investigate whether this framework is capable of identifying emergent behavior. The SEED methodology is used to explore the system design space, including the number of UAVs used in the system and influential vehicle and system parameters. The results show that this methodology provides insights into the landscape of system performance across the design space. More specifically, it identifies a number of candidate designs which exhibit emergent behavior where the system performance rapidly improves as the system undergoes a transition from disorganized to organized behavior. The SEED methodology provides for a more rigorous, traceable, and thorough design process for systems which have been difficult to understand and design using traditional engineering methods.Ph.D

    Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

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    The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities

    Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

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    Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine thinking. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASAs Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agents active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems

    Towards adaptive multi-robot systems: self-organization and self-adaptation

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    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

    Organization of Multi-Agent Systems: An Overview

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    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
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