A Multi-agent Hybrid Approach to Decision Support in Job Groups Handling

2015 ◽  
pp. 80-89 ◽  
Author(s):  
Jarosław Wikarek ◽  
Izabela Ewa Nielsen
Keyword(s):  
Decision Support ◽  
Hybrid Approach ◽  
Multi Agent

Hybrid argumentation systems for structured news reports

2001 ◽  
Vol 16 (4) ◽  
pp. 295-329 ◽  
Author(s):  
ANTHONY HUNTER
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Hybrid Systems ◽  
Real World ◽  
Hybrid Approach ◽  
Graphical Notation ◽  
The Real ◽  
News Reports ◽  
Further Development ◽  
Argumentation Systems

Numerous argumentation systems have been proposed in the literature. Yet there often appears to be a shortfall between proposed systems and possible applications. In other words, there seems to be a need for further development of proposals for argumentation systems before they can be used widely in decision-support or knowledge management. I believe that this shortfall can be bridged by taking a hybrid approach. Whilst formal foundations are vital, systems that incorporate some of the practical ideas found in some of the informal approaches may make the resulting hybrid systems more useful. In informal approaches, there is often an emphasis on using graphical notation with symbols that relate more closely to the real-world concepts to be modelled. There may also be the incorporation of an argument ontology oriented to the user domain. Furthermore, in informal approaches there can be greater consideration of how users interact with the models, such as allowing users to edit arguments and to weight influences on graphs representing arguments. In this paper, I discuss some of the features of argumentation, review some key formal argumentation systems, identify some of the strengths and weaknesses of these formal proposals and finally consider some ways to develop formal proposals to give hybrid argumentation systems. To focus my discussions, I will consider some applications, in particular an application in analysing structured news reports.

An MDA based multi-agent approach for decision support systems development

2014 ◽  
Vol 6 (1) ◽  
pp. 221-231
Author(s):  
Abdelaziz El Fazziki ◽  
Abderrahmane Sadiq ◽  
Mohamed Sadgal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Systems Development ◽  
Multi Agent

scholarly journals Multi-Agent based Intelligent Decision Support Systems for Cancer Classification

2021 ◽  
Author(s):  
Hanaa Salem ◽  
Gamal Attiya ◽  
Nawal El-Fishawy
Keyword(s):  
Feature Selection ◽  
Decision Support ◽  
Machine Learning Algorithms ◽  
Final Report ◽  
Cad System ◽  
Intelligent Decision ◽  
Multi Agent ◽  
Microarray Datasets ◽  
Intelligent Decision Support ◽  
Cancer Diseases

There is evidence that early detection of cancer diseases can improve the treatment and increase the survival rate of patients. This paper presents an efficient CAD system for cancer diseases diagnosis by gene expression profiles of DNA microarray datasets. The proposed CAD system combines Intelligent Decision Support System (IDSS) and Multi-Agent (MA) system. The IDSS represents the backbone of the entire CAD system. It consists of two main phases; feature selection/reduction phase and a classification phase. In the feature selection/reduction phase, eight diverse methods are developed. While, in the classification phase, three evolutionary machine learning algorithms are employed. On the other hand, the MA system manages the entire operation of the CAD system. It first initializes several IDSSs (exactly 24 IDSSs) with the aid of mobile agents and then directs the generated IDSSs to run concurrently on the input dataset. Finally, a master agent selects the best classification, as the final report, based on the best classification accuracy returned from the 24 IDSSs The proposed CAD system is implemented in JAVA, and evaluated by using three microarray datasets including; Leukemia, Colon tumor, and Lung cancer. The system is able to classify different types of cancer diseases accurately in a very short time. This is because the MA system invokes 24 different IDSS to classify the diseases concurrently in parallel processing manner before taking the decision of the best classification result.

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