Hybrid Multi-Agent Systems

Srinivasan, Dipti; Choy, Min Chee

doi:10.1007/978-3-642-14435-6_2

Cited by 10 publications

(4 citation statements)

References 31 publications

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“…Cooperation requires nodes to work together toward the achievement of their individual or common goals, while coordination refers to managing interdependencies (e.g., shared resource access). In cooperative control, while the objective of the network is global, the nodes solve the optimization problem locally and display a high level of interaction [207]. In non-cooperative control, nodes work toward their own, independent objectives [208].…”

Section: Emergence and Consensus Through Decentralized Control In Com...mentioning

confidence: 99%

Engineering Emergence: A Survey on Control in the World of Complex Networks

Berceanu

Pătraşcu

2022

Automation

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Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.

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Section: Emergence and Consensus Through Decentralized Control In Com...mentioning

confidence: 99%

Engineering Emergence: A Survey on Control in the World of Complex Networks

Berceanu

Pătraşcu

2022

Automation

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“…Srinivasan and Jain [84] stated that the intelligent behavior of an agent may be limited given the extreme difficulty in developing knowledge structures and rule bases, which completely describe the task of the agent if the task is complex by nature. This problem can be partially overcome by causing the agent to learn on its own during this task.…”

Section: Online Learningmentioning

confidence: 99%

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Sönmez,

Rutherford,

Valavanis

2024

Drones

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Multirotor UAVs are used for a wide spectrum of civilian and public domain applications. Their navigation controllers include onboard sensor suites that facilitate safe, autonomous or semi-autonomous multirotor flight, operation, and functionality under nominal and detrimental conditions and external disturbances, even when flying in uncertain and dynamically changing environments. During the last decade, given the available computational power, different learning-based algorithms have been derived, implemented, and tested to navigate and control, among other systems, multirotor UAVs. Learning algorithms have been and are used to derive data-driven based models, to identify parameters, to track objects, to develop navigation controllers, and to learn the environments in which multirotors operate. Learning algorithms combined with model-based control techniques have proven beneficial when applied to multirotors. This survey summarizes the research published since 2015, dividing algorithms, techniques, and methodologies into offline and online learning categories and then further classifying them into machine learning, deep learning, and reinforcement learning sub-categories. An integral part and focus of this survey is on online learning algorithms as applied to multirotors, with the aim to register the type of learning techniques that are either hard or almost hard real-time implementable, as well as to understand what information is learned, why, how, and how fast. The outcome of the survey offers a clear understanding of the recent state of the art and of the type and kind of learning-based algorithms that may be implemented, tested, and executed in real time.

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“…The demand of autonomous MAS's with the ability to perform tasks as Decision-Making or Policy Formulation leads to hybrid MAS's. There are various different applications of hybrid MAS's and many different ways to describe them, e. g. (Srinivasan and Choy, 2010). How a hybrid MAS is modeled as tensor system is shown in the following.…”

Section: Multi-agent Systems Examplementioning

confidence: 99%

“…Another field of applications are distributed network systems like Multi-Agent Systems (MAS), which have received considerable attention over the past ten years, because of its broad variety of applications, see (Ren and Beard, 2007;Ren and Cao, 2011). To perform tasks like Decision-Making or Policy Formulation, hybrid MASs are required, (Srinivasan and Choy, 2010). These can be represented by the same class of hybrid tensor systems.…”

Section: Introductionmentioning

confidence: 99%

Tensor Systems - Multilinear Modeling and Applications

Pangalos¹,

Eichler²,

Lichtenberg³

2013

Proceedings of the 3rd International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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Tensor systems are a framework for modeling of multilinear hybrid systems with discrete and continuous valued signals. Two examples from building services engineering and multi-agent systems show applications of this framework. A tensor model of a heating system is derived and approximated by tensor decomposition methods first. Second, a tensor model of a multi-agent system with a structure already given in a decomposed form is reduced further by the same decomposition methods. The validity of these low rank approximations is shown for both examples.

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Hybrid Multi-Agent Systems

Cited by 10 publications

References 31 publications

Engineering Emergence: A Survey on Control in the World of Complex Networks

Engineering Emergence: A Survey on Control in the World of Complex Networks

A Survey of Offline- and Online-Learning-Based Algorithms for Multirotor Uavs

Tensor Systems - Multilinear Modeling and Applications

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