A Multi-Agent System for Industrial Simulators Design

Redjimi, Kenza; Redjimi, Mohammed

doi:10.1007/978-3-030-85365-5_13

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Different platforms like JADE [17][18][19][20], ZEUS [21][22][23], Malaca [24], Mad-Kit [25][26][27], Janus [28,29], NetLogo [30,31], and many more [32][33][34][35][36] have been used in the literature in order to implement MAS. Table 1 organizes the different platforms and references.…”

Section: Related Workmentioning

confidence: 99%

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On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

Siatras,

Bakopoulos,

Mavrothalassitis

et al. 2023

Applied Sciences

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Industry 4.0 (I4.0) aims at achieving the interconnectivity of multiple industrial assets from different hierarchical layers within a manufacturing environment. The Asset Administration Shell (AAS) is a pilar component of I4.0 for the digital representation of assets and can be applied in both physical and digital assets, such as enterprise software, artificial intelligence (AI) agents, and databases. Multi-agent systems (MASs), in particular, are useful in the decentralized optimization of complex problems and applicable in various planning or scheduling scenarios that require the system’s ability to adapt to any given problem by using different optimization methods. In order to achieve this, a universal model for the agent’s information, communication, and behaviors should be provided in a way that is interoperable with the rest of the I4.0 assets and agents. To address these challenges, this work proposes an AAS-based information model for the description of scheduling agents. It allows multiple AI methods for scheduling, such as heuristics, mathematical programming, and deep reinforcement learning, to be encapsulated within a single agent, making it adjustable to different production scenarios. The software implementation of the proposed architecture aims to provide granularity in the deployment of scheduling agents which utilize the underlying AAS metamodel. The agent was implemented using the SARL agent-oriented programming (AOP) language and deployed in an open-source MAS platform. The system evaluation in a real-life bicycle production scenario indicated the agent’s ability to adapt and provide fast and accurate scheduling results.

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Section: Related Workmentioning

confidence: 99%

“…[ [25][26][27] Malaca Malaca is a gent architecture that combines Component-based Software Engineering and Aspect-Oriented Software Development.…”

Section: Zeusmentioning

confidence: 99%

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

Siatras,

Bakopoulos,

Mavrothalassitis

et al. 2023

Applied Sciences

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“…In [32], the authors used concept of AGR model to represent a liquefied natural gas treatment process (decarbonation) at a gas plant in Algeria. Moreover in [30], the authors prposed a transformation of Agent-Group-Role (AGR) organizational model in a categorical way in order to obtain a formal semantics model describing the MAS organization, which allows a high level of abstraction.…”

Section: Related Workmentioning

confidence: 99%

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game

Sid,

Djezzar,

Souidi

et al. 2023

cai

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Pursuit-Evasion Game (PEG) can be defined as a set of agents known as pursuers, which cooperate with the aim forming dynamic coalitions to capture dynamic evader agents, while the evaders try to avoid this capture by moving in the environment according to specific velocities. The factor of capturing time was treated by various studies before, but remain the powerful tools used to satisfy this factor object of research. To improve the capturing time factor we proposed in this work a novel online decentralized coalition formation algorithm equipped with Convolutional Neural Network (CNN) and based on the Iterated Elimination of Dominated Strategies (IEDS). The coalition is formed such that the pursuer should learn at each iteration the approximator formation achieving the capture in the shortest time. The pursuer's learning process depends on the features extracted by CNN at each iteration. The proposed supervised technique is compared through simulation, with the IEDS algorithm, AGR algorithm. Simulation results show that the proposed learning technique outperform the IEDS algorithm and the AGR algorithm with respect to the learning time which represents an important factor in a chasing game.

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“…Considering the agent as a computational system located in an environment that can act independently to achieve its design goals in that environment, the interoperability of multi-agent systems implies that each agent can be influenced by other agents or humans to carry out tasks in accordance with their goals [32][33][34]. To this end, it can be concluded that the current framework's role for ERP modules and their performance, including autonomous data exchange between them, has been upgraded to agents and multi-agent systems.…”

Section: Integration With Erpmentioning

confidence: 99%

Multi-Agent Enterprise Resource Planning Production Control (MAERPPC) Methodology Based on Personnel Health Monitoring

Faghihi

Kazerooni

2023

Scientia Iranica

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A Multi-Agent System for Industrial Simulators Design

Cited by 3 publications

References 17 publications

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

On the Use of Asset Administration Shell for Modeling and Deploying Production Scheduling Agents within a Multi-Agent System

New Game-Theoretic Convolutional Neural Network Applied for the Multi-Pursuer Multi-Evader Game

Multi-Agent Enterprise Resource Planning Production Control (MAERPPC) Methodology Based on Personnel Health Monitoring

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