A Systematic Literature Review in Multi-Agent Systems: Patterns and Trends

Falco, Mariana; Robiolo, Gabriela

doi:10.1109/clei47609.2019.235098

Cited by 11 publications

(6 citation statements)

References 19 publications

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“…By reachable, we understand having a value other than 0 in the specified column of the M t K matrix. Definitions of both matrices are listed below: (2,0) tuple in the first column of M 0 K matrix denotes that we have two objects. The zeros in both tuples indicate that each object starts the mission at the same moment.…”

Section: Behavior Policymentioning

confidence: 99%

“…Functions tupled with each state allow to "track" task elements between states. For example, the function m s = (0, 0), (1,1), (2,2), (3,3), (4,4) for the state at time 0 indicates that the object tagged with the unique identifier 2 (the argument of m s function) is represented by the vertex with identifier 2 (the value of m s function for argument 2). The support of a bigraph itself does not track its elements between transitions, as can be seen by comparing the state of the system at time 0 and time 1.…”

Section: Phasementioning

confidence: 99%

“…The concept of agent is applied to entities that have autonomy and are placed in a changing environment. Multi-agent systems [1,2] are structures within which agents can be identified. One of the advantages of designs using agents is that they can be represented at different levels of detail, from abstract entities (like mathematical structures) to actual robots.…”

Section: Introductionmentioning

confidence: 99%

“…result = Phase4(s c , m c , r, s 0 , m 0 , n x ) Phase 4 completed without error 3 s c , m c , n x = result s c = m c = {(0, 3), (3, 0), (4, 1), (1, 4), (2, s, m s , i s = result W c = ∅ s = m s = {(0, 3), (3, 0), (4, 1),(1,4),(2,2)…”

mentioning

confidence: 99%

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Design and Verification of Multi-Agent Systems with the Use of Bigraphs

Cybulski

Zieliński

2021

Applied Sciences

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Widespread access to low-cost, high computing power allows for increased computerization of everyday life. However, high-performance computers alone cannot meet the demands of systems such as the Internet of Things or multi-agent robotic systems. For this reason, modern design methods are needed to develop new and extend existing projects. Because of high interest in this subject, many methodologies for designing the aforementioned systems have been developed. None of them, however, can be considered the default one to which others are compared to. Any useful methodology must provide some tools, versatility, and capability to verify its results. This paper presents an algorithm for verifying the correctness of multi-agent systems modeled as tracking bigraphical reactive systems and checking whether a behavior policy for the agents meets non-functional requirements. Memory complexity of methods used to construct behavior policies is also discussed, and a few ways to reduce it are proposed. Detailed examples of algorithm usage have been presented involving non-functional requirements regarding time and safety of behavior policy execution.

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Section: Behavior Policymentioning

confidence: 99%

Section: Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Design and Verification of Multi-Agent Systems with the Use of Bigraphs

Cybulski

Zieliński

2021

Applied Sciences

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“…In the last half decade, there were very limited reviews which gives a general overview of the current frameworks, technologies, applications and challenges of multi-agent based manufacturing. Falco and Robiolo [2] presented a detailed systematic review of the patterns and trends in MAS with focus on all application domains including transport, healthcare and manufacturing. Calegari et al [3] presented a systematic review of MAS but mainly focused on logical technologies.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent based manufacturing: current trends and challenges

Pulikottil

Estrada-Jimenez

Rehman

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

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The advent of Industry 4.0 and the development of future industrial applications can be achieved using Cyber-Physical Systems (CPS). This technological development invokes high levels of communication and computation in the form of an interconnected network of industrial resources. Multi-agent systems precisely can empower such technological evolution by introducing properties like decentralisation, autonomy, flexibility, social ability and modularity to the industrial context. In this regard, the current work surveys recent multi-agent based manufacturing approaches and provides a general vision of current trends focusing on frameworks/architectures, complementary technologies and common applications. This article, ends with an integrated discussion of emerging agent-based industrial challenges, a general conclusion and final remarks.

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