A comparative analysis of negotiation methods for a multi-neural agent system

Abreu, Márjory Da Costa; Canuto, Anne M. P.; Santana, Laura E.

doi:10.1109/ichis.2005.3

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…Also, an agent may decide to perform the decision making process one more time, analyzing other criteria or pattern features. There is always a feedback to all agents, containing the ranking of the agents at the end of the negotiation (more details, see [12,13]). …”

Section: The Neurage Systemmentioning

confidence: 99%

“…In this sense, they have to be adjusted in order to be used in the NeurAge system. In [12], three negotiation protocols for the NeurAge system were presented.…”

Section: Introductionmentioning

confidence: 99%

“…The NeurAge system has been successfully applied to some classification tasks, such as in [4,12]. However, this system has only used MLPs as the neural network modules of its agents.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Benefits of Using a Fuzzy-Neuro Model in the Accuracy of the NeurAge System: an Agent-Based System for Classification Tasks

Abreu

Canuto

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system is composed by several neural agents which communicate (negotiate) a common result for the testing patterns. The NeurAge system has been successfully applied in some classification tasks. Basically, in these investigations, NeurAge has used multi-layer perceptrons (MLPs) as the neural network module of its agents. In this paper, it is presented an investigation of the use of the NeurAge System using other types of classifiers, mainly Fuzzy MLP. The main aim of this investigation is to analyze the benefits of using fuzzy neural networks in the performance of the NeurAge System.

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Section: The Neurage Systemmentioning

confidence: 99%

“…In this sense, they have to be adjusted in order to be used in the NeurAge system. In [12], three negotiation protocols for the NeurAge system were presented.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the Benefits of Using a Fuzzy-Neuro Model in the Accuracy of the NeurAge System: an Agent-Based System for Classification Tasks

Abreu

Canuto

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks

et al. 2008

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Analyzing the Performance of an Agent-based Neural System for Classification Tasks Using Data Distribution among the Agents

Santana

Canuto

Abreu

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system has presented good results in some conventional (centralized) classification tasks. Nevertheless, in some classification tasks, relevant features can be distributed over a set of agent. These applications can be classified as distributed classification tasks. In this paper, an investigation of the performance of the NeurAge system in distributed classification tasks will be performed. In other words, it will be investigated the performance of NeurAge when the data are distributed over its agents.

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A comparative analysis of negotiation methods for a multi-neural agent system

Cited by 9 publications

References 15 publications

Analyzing the Benefits of Using a Fuzzy-Neuro Model in the Accuracy of the NeurAge System: an Agent-Based System for Classification Tasks

Analyzing the Benefits of Using a Fuzzy-Neuro Model in the Accuracy of the NeurAge System: an Agent-Based System for Classification Tasks

An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks

Analyzing the Performance of an Agent-based Neural System for Classification Tasks Using Data Distribution among the Agents

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