Conferences related to Multiagent systems

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2020 IEEE 16th International Workshop on Advanced Motion Control (AMC)

AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.


Oceans 2020 MTS/IEEE GULF COAST

To promote awareness, understanding, advancement and application of ocean engineering and marine technology. This includes all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.

  • OCEANS '96

  • OCEANS '97

  • OCEANS '98

  • OCEANS '99

  • OCEANS 2000

  • OCEANS 2001

  • OCEANS 2002

  • OCEANS 2003

  • OCEANS 2004

  • OCEANS 2005

  • OCEANS 2006

  • OCEANS 2007

  • OCEANS 2008

    The Marine Technology Society (MTS) and the Oceanic Engineering Society (OES) of the Institute of Electrical and Electronic Engineers (IEEE) cosponsor a joint conference and exposition on ocean science, engineering, education, and policy. Held annually in the fall, it has become a focal point for the ocean and marine community to meet, learn, and exhibit products and services. The conference includes technical sessions, workshops, student poster sessions, job fairs, tutorials and a large exhibit.

  • OCEANS 2009

  • OCEANS 2010

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2011

    The Marine Technology Society and the Oceanic Engineering Scociety of the IEEE cosponsor a joint annual conference and exposition on ocean science engineering, and policy.

  • OCEANS 2012

    Ocean related technology. Tutorials and three days of technical sessions and exhibits. 8-12 parallel technical tracks.

  • OCEANS 2013

    Three days of 8-10 tracks of technical sessions (400-450 papers) and concurent exhibition (150-250 exhibitors)

  • OCEANS 2014

    The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2015

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor a joint annual conference and exposition on ocean science, engineering, and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 150-200 exhibits.

  • OCEANS 2016

    The Marine Technology Scociety and the Oceanic Engineering Society of the IEEE cosponor a joint annual conference and exposition on ocean science, engineering, and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 500 technical papers and 150 -200 exhibits.

  • OCEANS 2017 - Anchorage

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town meetings on policy and governmental process.

  • OCEANS 2018 MTS/IEEE Charleston

    Ocean, coastal, and atmospheric science and technology advances and applications


2020 IEEE 18th International Conference on Industrial Informatics (INDIN)

INDIN focuses on recent developments, deployments, technology trends, and research results in Industrial Informatics-related fields from both industry and academia


2020 IEEE International Symposium on Circuits and Systems (ISCAS)

The International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premier networking and exchange forum for researchers in the highly active fields of theory, design and implementation of circuits and systems. ISCAS2020 focuses on the deployment of CASS knowledge towards Society Grand Challenges and highlights the strong foundation in methodology and the integration of multidisciplinary approaches which are the distinctive features of CAS contributions. The worldwide CAS community is exploiting such CASS knowledge to change the way in which devices and circuits are understood, optimized, and leveraged in a variety of systems and applications.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.



Periodicals related to Multiagent systems

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Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Control Systems Technology, IEEE Transactions on

Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.


Distributed Systems Online, IEEE

After nine years of publication, DS Online will be moving into a new phase as part of Computing Now (http://computingnow.computer.org), a new website providing the front end to all of the Computer Society's magazines. As such, DS Online will no longer be publishing standalone peer-reviewed articles.


Evolutionary Computation, IEEE Transactions on

Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evolutionary neurocomputing, evolutionary fuzzy systems, applications in biomedicine and biochemistry, robotics and control, mathematical modelling, civil, chemical, aeronautical, and industrial engineering applications.



Most published Xplore authors for Multiagent systems

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Xplore Articles related to Multiagent systems

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Multi-Agent System in Urban Traffic Signal Control

IEEE Computational Intelligence Magazine, 2010

Multi-agent system is a rapidly developing field of distributed artificial intelligence that has gained significant importance because of its ability to solve complex real-world problems. It provides a highly flexible and modular structure, which incorporates the domain expertise in the system, to achieve the optimal solution. Multi-agent system also allows a problem to be divided into smaller sub-problems that require ...


What's needed to build team players? (Extended abstract for an invited talk)

Proceedings Fourth International Conference on MultiAgent Systems, 2000

In this talk, I will briefly review the major features of one model of collaborative planning, SharedPlans. The model provides a framework in which to raise and address fundamental questions about collaboration and the construction of collaboration-capable agents. I will discuss recent approaches to three plan management processes - assessment of alternatives, commitment management, and group decision-making for recipe selection ...


Urban traffic multi-agent system based on RMM and Bayesian learning

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000

Addresses multi-agent coordination in urban traffic control to coordinate the signals of adjacent intersections for minimizing the waiting car queue in the urban traffic network. For the purpose of this case study, we adopt a multi- agent coordination, which uses the recursive modeling method (RMM) that enables an agent to select his rational action by examining with other agents by ...


Learning acceleration by policy sharing

2011 9th World Congress on Intelligent Control and Automation, 2011

Reinforcement learning is one of the more prominent machine learning technologies due to its unsupervised learning structure and ability to continually learn, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from the ...


Research of task re-allocation mechanism in agile supply chain based on combinatorial auction

Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527), 2002

In multi-agent system based agile supply chain management, there are many complex task re-allocation problems at the transaction level. In this paper, we use the combinatorial auction theory to solve these problems. We first analyze the features of task re-allocation problems and build a problem model, and then design a comprehensive task re-allocation mechanism based on combinatorial auction. Some further ...



Educational Resources on Multiagent systems

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IEEE.tv Videos

Risto Miikkilainen - Multiagent Learning Through Neuroevolution
IMS 2011 Microapps - Calibration and Accuracy in Millimeter Systems
Open Systems Architecture for RF and Microwave Technologies: MicroApps 2015 - Mercury Systems
Harold "Bud" Lawson - IEEE Simon Ramo Medal, 2019 IEEE Honors Ceremony
A Thermodynamic Treatment of Intelligent Systems - IEEE Rebooting Computing 2017
Inside Kiva Systems - Warehouse Robots at Work
ITEC 2014: Urban Mass Transit Systems: Current Status and Future Trends
Impact on Society: Systems Engineer to Systems Entrepreneur for Global Change - Erna Grasz at the 2017 IEEE VIC Summit
IROS TV 2019- Maryland Robotics Center, Institute for Systems Research, University of Maryland
EMBC 2011-Workshop- Biological Micro Electro Mechanical Systems (BioMEMS): Fundamentals and Applications-Utkan Demirci
Innovative Mechanical Systems to Address Current Robotics Challenges
Continuously Learning Neuromorphic Systems with High Biological Realism: IEEE Rebooting Computing 2017
Rebooting Computing: Trust and Security in Future Computing Systems
EMBC 2011-Keynote Lecture-Engineering Drug Dosing in Dynamic Biological Systems - David J. Balaban
Augmented Reality in Operating Rooms
EMBC 2011-Program-Systems in Synthetic Biology (Part I)-Pamela A. Silver
Wireless Charging Systems for EVs
Research, Development and Field Test of Robotic Observation Systems for Active Volcanic Areas in Japan
IEEE Green Energy and Systems Conference 2015
Intelligent Transportation Systems Society: Changing how the world moves

IEEE-USA E-Books

  • Multi-Agent System in Urban Traffic Signal Control

    Multi-agent system is a rapidly developing field of distributed artificial intelligence that has gained significant importance because of its ability to solve complex real-world problems. It provides a highly flexible and modular structure, which incorporates the domain expertise in the system, to achieve the optimal solution. Multi-agent system also allows a problem to be divided into smaller sub-problems that require less domain expertise compared to solving the problem as a whole. In recent years, multi-agent system has gained significant attention in solving traffic signal control problems because of the advantages it offers in solving complex problems with uncertainties. In this paper, two different types of multi-agent architectures that have been implemented on a simulated complex urban traffic network in Singapore for adaptive intelligent signal control are discussed. The results obtained indicate the superior performance of the multi-agent signal controller in comparison to pre-timed and signal control methods which are currently in use.

  • What's needed to build team players? (Extended abstract for an invited talk)

    In this talk, I will briefly review the major features of one model of collaborative planning, SharedPlans. The model provides a framework in which to raise and address fundamental questions about collaboration and the construction of collaboration-capable agents. I will discuss recent approaches to three plan management processes - assessment of alternatives, commitment management, and group decision-making for recipe selection and task allocation - and will raise several challenges for future research.

  • Urban traffic multi-agent system based on RMM and Bayesian learning

    Addresses multi-agent coordination in urban traffic control to coordinate the signals of adjacent intersections for minimizing the waiting car queue in the urban traffic network. For the purpose of this case study, we adopt a multi- agent coordination, which uses the recursive modeling method (RMM) that enables an agent to select his rational action by examining with other agents by modeling their decision making in a distributed multi-agent environment. Bayesian learning is used in conjunction with RMM for belief update. As a result, an agent can determine which models of the other agents are correct, and keep his knowledge up to date. We describe how decision making using RMM and Bayesian learning is applied to the urban traffic control domain to settle a multi-agent traffic control system and show experimental results.

  • Learning acceleration by policy sharing

    Reinforcement learning is one of the more prominent machine learning technologies due to its unsupervised learning structure and ability to continually learn, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from the other agents in the system to increase the speed of learning can be accelerated. In the proposed learning algorithm, an agent store its experience in terms of state aggregation implemented with a decision tree, such that policy sharing between multi-agent is eventually accomplished by merging different decision trees between peers. Unlike lookup tables which have homogeneous structure for state aggregations, decision trees carried in agents are with heterogeneous structure. This work executes policy sharing between cooperative agents by means of forming a hyper structure from their trees instead of merging whole trees violently. The proposed scheme initially translates the whole decision tree from an agent to others. Based on the evidence, only partial leaf nodes hold helpful experience for policy sharing. The proposed method inducts a hyper decision tree by a great mount of samples which are sampled from the shared nodes. Results from simulations in multi-agent cooperative domain illustrate that the proposed algorithms perform better than the one without sharing.

  • Research of task re-allocation mechanism in agile supply chain based on combinatorial auction

    In multi-agent system based agile supply chain management, there are many complex task re-allocation problems at the transaction level. In this paper, we use the combinatorial auction theory to solve these problems. We first analyze the features of task re-allocation problems and build a problem model, and then design a comprehensive task re-allocation mechanism based on combinatorial auction. Some further research on the problem is also discussed.

  • A Decentralized Markovian Jump<formula formulatype="inline"><tex Notation="TeX">${\cal H}_{\infty}$</tex></formula>Control Routing Strategy for Mobile Multi-Agent Networked Systems

    This paper presents a Markovian jump linear (MJL) system framework for developing routing algorithms in mobile ad hoc networks (MANETs) that encounter changes in the number of nodes and/or the number of destinations. A unified H∞control strategy is proposed by representing the dynamically changing destination nodes as singular switching control systems. A decentralized routing scheme is proposed and designed for the networked multi- agent system in presence of unknown time-varying delays. To solve the corresponding optimization problem the physical constraints are expressed as linear matrix inequality (LMI) conditions. The resulting decentralized H∞routing control schemes for both regular and singular MJL systems are shown to formally achieve the desired performance specifications and requirements. Simulation results are presented to illustrate and demonstrate the effectiveness of our proposed novel routing control strategies.

  • Intelligent agent-based expert system architecture for generating work plan in marshalling station

    The present approach for generating work plan is difficult to meet the dynamic and intelligent requirement, and it has not had the function of assistant decision yet. An agent that can simulate the reasoning process of human has the properties such as adaptability and intelligence, which can be used to solve the problems in making plan process. This paper has presented the initial architecture for an intelligent agent-based expert system for generating of work plan in marshalling station known as IAES-OPMY. IAES-OPMY can support the station controller's decisions on generating plan and in turn improve the validity, encash ratio and automatization of plan. Furthermore, it can speed the turnover of wagons and bring great profits.

  • Coalition formation under uncertainty

    We address the problem of coalition formation in environments where resources consumption is uncertain.

  • A study on robot task planning problems in multiagent environments

    Attempts to realize an autonomous solving mechanism for a task planning problem by a machine learning system. As a robot task planning problem, the block stacking problem is treated. It is well known as one of the most difficult problems. In particular the difficulties of this problem increase in a multiagent environment where multiple robots exist in a problem domain and they cooperate or negotiate to achieve given tasks effectively. To realize an autonomous planning mechanism, a classifier system is applied to this problem. In this approach, a classifier corresponds to a production rule that instructs the next operation when its conditional part is fully matched for a current state. Each robot has an individual classifier system as the robot task planner.

  • Matchmaking based on task and capability description

    It is the foundation for agent cooperation that understanding between agents in a multi-agent environment. It implies that the service agents should describe their capabilities and the requisition agents or users describe their tasks in a suitable manner and make both of them to be known by the matchmakers and other agents before any advertisement, request or matchmaking took place. In this paper, we discussed the methods to describe task and capability and make match between them with domain ontology. Both task and capability can be described by input and output with parameters and constraints, so we can accomplish matchmaking by two steps, which are parameter implication and constraint satisfaction. The former deals with concept matching and the latter constraints matching. Meanwhile, domain ontology will play an important role.



Standards related to Multiagent systems

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No standards are currently tagged "Multiagent systems"