In this paper a multi agent platform targeted towards the area of computational intelligence modeling is presented. We show the design of various computational agents and multi agent systems, as well as the infrastructure capabilities. The focus of the system is the interchangeability of computational components, their autonomous behavior, and emergence of new models. It is demonstrated that su...

We compare structural different methods of the artificial intelligence for wind power prediction modeling and build additionally ensembles of the models. As input variables for these prediction methods weather data of a numerical weather prediction model are used. The performance of the presented methods is compared to the predictions of the neural network based model.

The methodology proposed in the paper applies artificial intelligence (AI) techniques to the modeling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity in such systems are, in fact, difficult to model and control using traditional techniques. The paper proposes a framework for the development of soft co...

Generalization problems arise in many branches of artificial intelligence: machine learning, analogical and case-based reasoning, cognitive modeling, knowledge discovery, etc. Anti-unification is a technique used often to solve generalization problems. In this paper we describe an open-source library of some newly developed anti-unification algorithms in various theories: for firstand second-or...

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Heavy-duty trucks contribute approximately 20% of fuel consumption in the United States America (USA). The economy heavy-duty vehicles (HDV) is affected by several real-world parameters like road parameters, driver behavior, weather conditions, and vehicle etc. Although modern comply with emissions regulations, potential malfunction engine, regular wear tear, or other factors could affect perfo...

Since the past few years, so-called pre-trained Language Models (PLM) are considered state-of-the-art in field of Natural Processing (NLP) and thus experiencing widespread successful application. In addition to traditional supervised Machine Learning (ML) tasks such as spam email or customer churn classification, this technology opens up advanced approaches unsupervised learning data analytics ...

Artificial intelligence methods have often been applied to perform specific functions or tasks in the cyber– defense realm. However, as adversary methods become more complex and difficult to divine, piecemeal efforts to understand cyber–attacks, and malware–based attacks in particular, are not providing sufficient means for malware analysts to understand the past, present and future characteris...