Interoperability model between heterogeneous MAS platforms based on mobile agent and reinforcement learning

Amrani, Noureddine El Abid; Snineh, Sidi Mohammed; Youssfi, Mohamed; Abra, Oum El Kheir; Bouattane, Omar

doi:10.1109/icds53782.2021.9626723

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Since the multi-agent system simultaneously solves the tasks of distributed data collection and related tasks of spatial self-organization, there is a problem with the functional coordination of the corresponding processes of controlling the behavior of a mobile agent. This problem has several aspects, in particular the degree and nature of the functional dependence of the corresponding algorithms, the degree of their dependence on the features of the mobile robotics platform of the agent [16], the possibility and degree of parallelization of these algorithms, methods of coordinating their simultaneous execution, etc. One of the most promising approaches to solving this problem is the study of methods of coscheduling spatial self-organization control processes and distributed data collection processes.…”

Section: Introductionmentioning

confidence: 99%

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

Botchkaryov¹

2022

ACPS

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The problem of coscheduling spatial self-organization control processes and distributed data collection processes in a multi-agent system has been considered. The goal of coscheduling is to find and use the possibilities of functional coordination of these processes and increase the efficiency of the multi-agent system due to their parallel execution. An analysis of the main features of spatial self-organization tasks that affect the solution of the problem of coscheduling has been carried out. Variants of the mobile agent robotic platform configuration and the problem of the dependence of spatial self-organization algorithms on the type of robotic platform have been considered. A method of coscheduling of spatial self-organization and distributed data collection by coordinated parallel execution of the corresponding data collection process and the process of controlling mobile agent motion has been proposed. The method of coscheduling is implemented using the interaction protocol of these processes and the algorithm for planning their parallel execution using functional decomposition. The simulation results of the proposed method of coscheduling are given. It is proved that the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent due to more efficient parallelization. On average, for typical values of parameters of control processes, the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent by 40.6 %.

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Section: Introductionmentioning

confidence: 99%

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

Botchkaryov¹

2022

ACPS

View full text Add to dashboard Cite

show abstract

Interoperability model between heterogeneous MAS platforms based on mobile agent and reinforcement learning

Cited by 1 publication

References 15 publications

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

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