Browsing by Author "Leblebicioğlu, Kemal"

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A new multi-agent decision making structure and application to model-based fault diagnosis problem
(IEEE, 2017) Leblebicioğlu, Kemal; Zengin, Yasin; Schmidt, Klaus Werner
A new hierarchical multi-agent decision-making structure has been proposed. There are two phases of the structure. The first phase is the construction phase where the decision making structure consisting of switching and classification agents is built on the training data set generated by the system scenarios. In construction phase, switching and classification agents are trained and made ready for decision making. In the decision phase, which is the second phase, the class of the new data sample is decided. This process is carried out by the transmission of the data sample to the correct classifier agent by the switching agents and the classification by the classifier agent. The proposed structure is applied to a complex fault identification problem and a successful result is obtained. The structure is also adaptable to other big data decision making problems.
Developing growing hierarchical structures for decision making
(IEEE, 2007) Beldek, Ulaş; Leblebicioğlu, Kemal; 59950
This study is about developing a hierarchical approach for decision-making problems. The development is done on a representative decision-making problem. A hierarchical decision making approach which enables fusion of decisions of previous and current levels is proposed. The agents that determine the decisions at different hierarchy levels is accomplished by utilizing a genetic algorithm.
Hierarchical decision making and decision fusion
(IEEE, 2007) Beldek, Ulaş; Leblebicioğlu, Kemal; 59950
In this study, a hierarchical decision making structure possessing a decision fusion technique is proposed in order to solve decision making problems efficiently. The proposed structure mainly depends on effects of the decisions made in the lower levels to decisions in the upper levels up to an activation degree. The proposed hierarchical structure is used for detecting the fault degrees for single and multiple fault scenarios artifically generated in a four tank system. The results obtained demonstrate the effectiveness of the proposed hierarchical decision making structure.
Strategy creation, decomposition and distribution in particle navigation
(Elsevier Science, 2007) Beldek, Ulaş; Leblebicioğlu, Kemal; 59950; 125952
Strategy planning is crucial to control a group to achieve a number of tasks in a closed area full of obstacles. In this study, genetic programming has been used to evolve rule-based hierarchical structures to move the particles in a grid region to accomplish navigation tasks. Communications operations such as receiving and sending commands between particles are also provided to develop improved strategies. In order to produce more capable strategies, a task decomposition procedure is proposed. In addition, a conflict module is constructed to handle the challenging situations and conflicts such as blockage of a particle's pathway to destination by other particles