Exploiting the Use of Cooperation in Self-Organizing Reliable Multiagent Systems

In this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach

Dynamic replication strategies in data grid systems: A survey

In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out

Implementing Fault-Tolerant Services in Goal-Oriented Multi-Agent Systems

WOS: 000340869800015In this paper, findings and analysis detail the implementation of fault tolerance services into a goal-oriented multi-agent systems development platform. Fault tolerance services are used to provide replication-based fault tolerance policies (i.e. static and adaptive) to multi-agent systems. This approach provided flexibility and reusability to multi-agent systems because fault tolerance policies were implemented as reusable plan structures. Thus, whenever an agent was needed to be made fault-tolerant, plans for fault tolerance policies were simply activated by sending a request message

EXPLOITING THE USE OF COOPERATION IN SELF-ORGANIZING RELIABLE MULTIAGENT SYSTEMS

WOS: 000457674100004In this paper, a novel and cooperative approach is exploited introducing a self-organizing engine to achieve high reliability and availability in multiagent systems. The Adaptive Multiagent Systems theory is applied to design adaptive groups of agents in order to build reliable multiagent systems. According to this theory, adaptiveness is achieved via the cooperative behaviors of agents and their ability to change the communication links autonomously. In this approach, there is not a centralized control mechanism in the multiagent system and there is no need of global knowledge of the system to achieve reliability. This approach was implemented to demonstrate its performance gain in a set of experiments performed under different operating conditions. The experimental results illustrate the effectiveness of this approach

Experience with feedback control mechanisms in self-replicating multi-agent systems

5th International Central and Eastern European Conference on Multi-Agent Systems -- SEP 25-27, 2007 -- Leipzig, GERMANYWOS: 000250900900014In this paper, we present an approach for adaptive replication to support fault tolerance. This approach uses a feedback control theory methodology within an adaptive replication infrastructure to determine replication degrees of replica groups. We implemented this approach in a multiagent system to survive Byzantine failures. At the end of the paper, we also provide some experimental results to show the effectiveness of our approach

Exploiting Pre-Trained Convolutional Neural Networks for the Detection of Nutrient Deficiencies in Hydroponic Basil

Due to the integration of artificial intelligence with sensors and devices utilized by Internet of Things technology, the interest in automation systems has increased. One of the common features of both agriculture and artificial intelligence is recommendation systems that increase yield by identifying nutrient deficiencies in plants, consuming resources correctly, reducing damage to the environment and preventing economic losses. The biggest shortcomings in these studies are the scarcity of data and the lack of diversity. This experiment aimed to identify nutrient deficiencies in basil plants cultivated in a hydroponic system. Basil plants were grown by applying a complete nutrient solution as control and non-added nitrogen (N), phosphorous (P) and potassium (K). Then, photos were taken to determine N, P and K deficiencies in basil and control plants. After a new dataset was created for the basil plant, pretrained convolutional neural network (CNN) models were used for the classification problem. DenseNet201, ResNet101V2, MobileNet and VGG16 pretrained models were used to classify N, P and K deficiencies; then, accuracy values were examined. Additionally, heat maps of images that were obtained using the Grad-CAM were analyzed in the study. The highest accuracy was achieved with the VGG16 model, and it was observed in the heat map that VGG16 focuses on the symptoms

Implementing a multi-agent organization that changes its fault tolerance policy at run-time

6th International Workshop on Engineering Societies in the Agents World -- OCT 26-28, 2005 -- Kusadasi, TURKEYWOS: 000238506800010In this paper, we present an approach that supports simultaneously applying different fault tolerance policies in multi-agent organizations. The main strategy of our approach is to implement fault tolerance policies as reusable agent plans using HTN (Hierarchical Task Network) formalism. In this way, different fault tolerance policies such as static and adaptive ones can be implemented as different plans. In a static fault tolerance policy, all parameters related to the fault tolerance are set by a programmer before run-time. However, an adaptive fault tolerance policy requires dynamically adapting resource allocation and replication mechanisms by monitoring the system. Monitoring of a system brings some cost to the system. If all agents in an organization apply the adaptive fault tolerance policy, the monitoring cost will become an important factor for the system performance. Hence by applying our approach, the adaptive policy can be applied only to the critical agents whose criticalities can be observed during the organization's lifetime and the static one can be applied to the remaining agents. This reduces the monitoring cost and increases the overall organization performance. A case study has been implemented to show the effectiveness of our approach

Parameter Tuning in Modeling and Simulations by Using Swarm Intelligence Optimization Algorithms

9th International Conference on Computational Intelligence and Communication Networks (CICN) -- SEP 16-17, 2017 -- Final Int Univ, Girne, CYPRUSWOS: 000432249700032Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results.. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.MIR Labs, IEEE Turkey Sec