Machine learning based Multi Agent Systems in Complex Networks

This research paper is an analysis on how to handle the complex multi agent systems. The technologies focused in this paper are the social multi agent systems. Moreover both complex and techno-social systems are related due to the major elements and features they possess such as the numerous level of organization, large number of entities, the pre-existing systems, and the autonomous units. Hence, the main remedy for curbing the control problem foreseen is centered on the paradigm of various multi-agents. Moreover, the paradigm if effectively designed in handling complex systems due to how it handles various description levels simultaneously. The major objective of this paper is pointing out the major query that emanates now the multi-agent patterns utilized during the regulation of various complex systems. In this paper, probable solutions will be presented by an equation, which exhibits open control design based on the multi-agent simulations. A unique example for the enactment of the prospected architecture has also been shown in the paper. The execution process of these elements is conducted considering a collective nature of the techno-social frameworks, such as open riding for peer-peer sharing of files within a common network. Various outcomes used in the implementation of the technology shows that the prospected architecture is in a position of taking control on the entire network. Lastly, the contribution made in this paper is making sure that the major queries have been identified during the process of utilizing the multi-agent prototype deployed for solving various complex frameworks, and targeted system units.