84,466 research outputs found
Integrating Peer-to-Peer Networking and Computing in the AgentScape Framework
The combination of peer-to-peer networking and agentbased computing seems to be a perfect match. Agents are cooperative and communication oriented, while peerto -peer networks typically support distributed systems in which all nodes have equal roles and responsibilities. AgentScape is a framework designed to support large-scale multi-agent systems. Pole extends this framework with peerto -peer computing. This combination facilitates the development and deployment of new agent-based peer-to-peer applications and services
Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles: Report 4 - Phase 1 Implementation of the Concept Demonstrator
This report describes the first phase of the implementation of the Concept Demonstrator. The Concept Demonstrator system is a powerful and flexible experimental test-bed platform for developing sensors, communications systems, and multi-agent based algorithms for an intelligent vehicle health monitoring system for deployment in aerospace vehicles. The Concept Demonstrator contains sensors and processing hardware distributed throughout the structure, and uses multi-agent algorithms to characterize impacts and determine an appropriate response to these impacts
Fractal patterns in fractionated spacecraft
Multi spacecraft architectures have been addressed in response to the demand for flexible architectures with high reliability and reduced costs compared to traditional monolithic spacecraft. A task that can be easily carried out by this kind of systems is the deployment of distributed antennas; these are composed of, typically, receiving elements carried on-board multiple spacecraft in precise formation. In this paper decentralised control means, based on artificial potential functions, together with a fractal-like connection network, are used to produce the autonomous and verifiable deployment and formation control of a swarm of spacecraft into a fractal-like pattern. The effect of using fractal-like routing of control data within the spacecraft generates complex formation shape patterns, while simultaneously reducing the amount of control information required to form such complex formation shapes. Furthermore, the techniques used ensure against swarm fragmentation, while exploiting communication channels anyway needed in a fractionated architecture. In particular, the superposition of potential functions operating at multiple levels (single agents, subgroups of agents, groups of agents) according to a self-similar adjacency matrix produces a fractal-like final deployment with the same stability property on each scale. Considering future high-precision formation flying and control capabilities, this paper considers, for the first time and as an example of a fractionated spacecraft, a decentralised multi-spacecraft fractal shaped antenna. A fractal antenna pattern provides multiple resonance peaks, directly related to the ratios of its characteristic physical lengths. Such a scenario would significantly improve the level of functionality of any multi-spacecraft synthetic aperture antenna system. Furthermore, multi-spacecraft architecture exploiting particular inter agent spacing can be considered to investigate multi-scale phenomena in areas such as cosmic radiation and space plasma physics. Both numerical simulations and analytic treatment are carried out demonstrating the feasibility of deploying and controlling a fractionated fractal antenna in space through autonomous decentralised means
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., & GarcÃa-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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Recommended from our members
Multi-agent system architectures for collaborative prognostics
This paper provides a methodology to assess the optimal Multi-Agent architecture for collaborative prognostics in modern fleets of assets. The use of Multi- Agent Systems has been shown to improve the ability to predict equipment failures by enabling machines with communication and collaborative learning capabilities. Di fferent architectures have been postulated for industrial Multi-Agent Systems in general. A rigorous analysis of the implications of their implementation for collaborative prognostics is essential to guide industrial deployment. In this paper, we investigate the cost and reliability implications of using di fferent Multi-Agent Systems architectures for collaborative failure prediction and maintenance optimization in large fleets of industrial assets. Results show that purely distributed architectures are optimal for high-value assets, while hierarchical architectures optimize communication costs for low-value assets. This enables asset managers to design and implement Multi-Agent systems for predictive maintenance that signi ficantly decrease the whole-life cost of their assets.The project that has generated these results has been supported by a la Caixa Fellowship (ID 100010434), with code LCF/BQ/EU17/11590049. This research was partly supported by Siemens Industrial Turbomachinery UK. This research was also partly supported by the Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and BT. The server used to perform the experiments in this paper was funded by the Centre for Digital Built Britain
Flexible Distributed Flocking Control for Multi-agent Unicycle Systems
Currently, the general aim of flocking and formation control laws for
multi-agent systems is to form and maintain a rigid configuration, such as, the
alpha-lattices in flocking control methods, where the desired distance between
each pair of connected agents is fixed. This introduces a scalability issue for
large-scale deployment of agents due to unrealizable geometrical constraints
and the constant need of centralized orchestrator to ensure the formation graph
rigidity. This paper presents a flexible distributed flocking cohesion
algorithm for nonholonomic multi-agent systems. The desired geometry
configuration between each pair of agents is adaptive and flexible. The
distributed flocking goal is achieved using limited information exchange (i.e.,
the local field gradient) between connected neighbor agents and it does not
rely on any other motion variables measurements, such as (relative) position,
velocity, or acceleration. Additionally, the flexible flocking scheme with
safety is considered so that the agents with limited sensing capability are
able to maintain the connectedness of communication topology at all time and
avoid inter-agent collisions. The stability analysis of the proposed methods is
presented along with numerical simulation results to show their effectiveness.Comment: 9 pages, 2 figure
Loosed coupled simulation of smart grid control systems
Smart grids rely on the integration of distributed energy resources towards an intelligent and distributed manner to organize the electrical power grid enabled by a bidirectional flow of information to improve reliability and robustness, fault detection and system operation, and plug-and-playability of energy devices. The integration of information and communication technologies (ICT), one of the key enablers of smart grids, will ease the deployment of intelligent and distributed systems implementing the automation functions. In this context, there is a need to assess how these systems, developed using these emergent technologies, e.g., multi-agent systems, data analytics and machine learning, will behave and affect the working conditions of the power grid. This paper aims to explore the development of a transparent and loose-coupled interface between the behavioral control system and the physical or simulated power system environment, in a coupled simulation perspective, aiming to assess and improve the development of such systems during the design phaseinfo:eu-repo/semantics/publishedVersio
Recommended from our members
Multi-agent system architectures for collaborative prognostics
Funder: Siemens Industrial Turbomachinery UKAbstract: This paper provides a methodology to assess the optimal multi-agent architecture for collaborative prognostics in modern fleets of assets. The use of multi-agent systems has been shown to improve the ability to predict equipment failures by enabling machines with communication and collaborative learning capabilities. Different architectures have been postulated for industrial multi-agent systems in general. A rigorous analysis of the implications of their implementation for collaborative prognostics is essential to guide industrial deployment. In this paper, we investigate the cost and reliability implications of using different multi-agent systems architectures for collaborative failure prediction and maintenance optimization in large fleets of industrial assets. Results show that purely distributed architectures are optimal for high-value assets, while hierarchical architectures optimize communication costs for low-value assets. This enables asset managers to design and implement multi-agent systems for predictive maintenance that significantly decrease the whole-life cost of their assets
Collaborative Multi-Agent Video Fast-Forwarding
Multi-agent applications have recently gained significant popularity. In many
computer vision tasks, a network of agents, such as a team of robots with
cameras, could work collaboratively to perceive the environment for efficient
and accurate situation awareness. However, these agents often have limited
computation, communication, and storage resources. Thus, reducing resource
consumption while still providing an accurate perception of the environment
becomes an important goal when deploying multi-agent systems. To achieve this
goal, we identify and leverage the overlap among different camera views in
multi-agent systems for reducing the processing, transmission and storage of
redundant/unimportant video frames. Specifically, we have developed two
collaborative multi-agent video fast-forwarding frameworks in distributed and
centralized settings, respectively. In these frameworks, each individual agent
can selectively process or skip video frames at adjustable paces based on
multiple strategies via reinforcement learning. Multiple agents then
collaboratively sense the environment via either 1) a consensus-based
distributed framework called DMVF that periodically updates the fast-forwarding
strategies of agents by establishing communication and consensus among
connected neighbors, or 2) a centralized framework called MFFNet that utilizes
a central controller to decide the fast-forwarding strategies for agents based
on collected data. We demonstrate the efficacy and efficiency of our proposed
frameworks on a real-world surveillance video dataset VideoWeb and a new
simulated driving dataset CarlaSim, through extensive simulations and
deployment on an embedded platform with TCP communication. We show that
compared with other approaches in the literature, our frameworks achieve better
coverage of important frames, while significantly reducing the number of frames
processed at each agent.Comment: IEEE Transactions on Multimedia, 2023. arXiv admin note: text overlap
with arXiv:2008.0443
Immune System Based Control and Intelligent Agent Design for Power System Applications
The National Academy of Engineering has selected the US Electric Power Grid as the supreme engineering achievement of the 20th century. Yet, this same grid is struggling to keep up with the increasing demand for electricity, its quality and cost. A growing recognition of the need to modernize the grid to meet future challenges has found articulation in the vision of a Smart Grid in using new control strategies that are intelligent, distributed, and adaptive. The objective of this work is to develop smart control systems inspired from the biological Human Immune System to better manage the power grid at the both generation and distribution levels. The work is divided into three main sections. In the first section, we addressed the problem of Automatic Generation Control design. The Clonal Selection theory is successfully applied as an optimization technique to obtain decentralized control gains that minimize a performance index based on Area Control Errors. Then the Immune Network theory is used to design adaptive controllers in order to diminish the excess maneuvering of the units and help the control areas comply with the North American Electric Reliability Corporation\u27s standards set to insure good quality of service and equitable mutual assistance by the interconnected energy balancing areas. The second section of this work addresses the design and deployment of Multi Agent Systems on both terrestrial and shipboard power systems self-healing using a novel approach based on the Immune Multi-Agent System (IMAS). The Immune System is viewed as a highly organized and distributed Multi-Cell System that strives to heal the body by working together and communicating to get rid of the pathogens. In this work both simulation and hardware design and deployment of the MAS are addressed. The third section of this work consists in developing a small scale smart circuit by modifying and upgrading the existing Analog Power Simulator to demonstrate the effectiveness of the developed technologies. We showed how to develop smart Agents hardware along with a wireless communication platform and the electronic switches. After putting together the different designed pieces, the resulting Multi Agent System is integrated into the Power Simulator Hardware. The multi Agent System developed is tested for fault isolation, reconfiguration, and restoration problems by simulating a permanent three phase fault on one of the feeder lines. The experimental results show that the Multi Agent System hardware developed performed effectively and in a timely manner which confirms that this technology is very promising and a very good candidate for Smart Grid control applications
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