Intelligent Multi Agent Systems for decision support in insurance industry

Markić, Ivan; Štula, Maja; Maras, Josip

doi:10.1109/mipro.2014.6859736

Cited by 6 publications

(3 citation statements)

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“…Intelligent agents are regarded as a computer intelligence paradigm. Multi-Agent Systems (MAS) are widely applicable, namely in decision support [20], navigation [21], industrial steel processing [22], and power systems [23]. In this paper, MAS was studied as an approach that can be used to ensemble ELM networks, where each ELM acted as individual agent, and MAS structure merged with the entire predictions of ELM to create a classification system that performed well.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

et al. 2020

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Extreme Learning Machine improved the iterative procedures of adjusting weights by randomly selecting hidden neurons besides analytically determining the output weights. In this paper, the basic ELM neural network was enhanced with a simplified network structure to achieve regression performance. Next, to solve the pattern classification, a hybrid system was proposed which integrated the ELM neural network and MAS models. A MAS model is then designed with a novel trust measurement method to combine ELM neural networks. Firstly, ELM hybrid with Single Input Rule Module (SIRM-ELM) was designed. There was only a single input connected to the rules, where the rules were the hidden neurons of ELM and each represented a single input fuzzy rules. Results showed that the SIRM-ELM model was better than Support Vector Machine and traditional ELM. Secondly, an extreme learning machine based multi agent systems (ELM-MAS) was designed to improve ELM's capability. Its first layer was made up of at least one ELM where ELM acted as an individual agent, whereas another layer was made up of a single ELM acting as the parent agent. Lastly, Certified Belief in Strength (CBS) method was applied to the ELM neural network to form ELM-MAS-CBS, using the reputation and strength of individual agents as the trust measurement. The assembly of strong elements related to the ELM agents formed the trust management that allowed the improvement of the performance in MAS using the CBS method. Both of the developed models were evaluated on its application on the power generation system. The test accuracy rate of both models for circulating water systems was shown to be comparable to other algorithms. In short, the developed models had been verified using benchmark datasets and applied in power generation, where the results were satisfactory.

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Section: Introductionmentioning

confidence: 99%

Enhancement of Neural Network Based Multi Agent System for Classification and Regression in Energy System

et al. 2020

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“…MAS belong to the field of Artificial Intelligence, the study addressing the approaches of construction of complex systems using a large number of entities, which alter their behavior in order to accommodate with a particular problem [1], [2]. An intelligent agent can be reactive and proactive, [3] because it responses to the actions and alteration which appears in the working environment, can tack the initiative to establish the goals and interacts with other agents [4], [1], [5].…”

Section: Introductionmentioning

confidence: 99%

“…An intelligent agent can be reactive and proactive, [3] because it responses to the actions and alteration which appears in the working environment, can tack the initiative to establish the goals and interacts with other agents [4], [1], [5]. Most literatures refer that the complexity emerges clearly in architecture design of multi agent systems that assigned many and different tasks [6], [7], [8].The research work introduces an approach to increase the architecture design quality of MAS by minimizing the effect of complexity.…”

Section: Introductionmentioning

confidence: 99%