Today, the term, Working Memory, is closely associated with intelligence. We propose that in addition to improving and speeding-up analysis, Artificial Intelligence (AI) can also be useful as a supplement to Working Memory. It is generally accepted that working memory plays a crucial role in cognition and models by computers, can help us understand the human mind. Building an artificial working memory can bring further benefits; for example, it can separate retrieval from reasoning and therefore, can acquire new concepts. The aim of this research is to solve the capacity shortage problem of Working Memory, by using AI as a supplement. In order to develop our argument, we characterize the ID3 algorithm as a way of looking for a consistent solution in the existing Case Based Graph; as the ID3 algorithm builds it from an empty graph, to an increasingly complex one. Methodologically, our study is based on observation of several Digital Natives (DNs) playing different games at Mobilis Interactive Exhibition Center in Gyor, Hungary. The aim is to explore the behavior of the DN generation. By identifying the different mindset patterns of DNs, we will be able to observe how different DNs can be facilitated, to enjoy the games, rather than being bored, anxious or even, becoming addicted.

Leontief Input/Output tables are widely used for solving various economic problems. This algorithm predicts the effect of changes in an economy sector on others. The same model can also be used for solving traffic forecasting problems. The solution algorithm applied is the so-called RAS one where a certain exponential opposition function is used. A simple and fast algorithm for the determination of the parameters of this opposition function will be presented. © 2013 IEEE.