Conclusions and future directions

Abstract

In this book, we presented a comprehensive analysis of the principal tasks related to the intelligent models of random walk based on learning automata. After introducing the random walk, we focused on its main drawbacks and weak performance in real-world applications. Then, three intelligent models were established on the bases of random walk. Moreover, theoretical analysis and convergence behavior of the proposed models based on weak convergence theory and Ordinary Differential Equation (ODE) were studied. In addition, the proposed models were applied in two large-scale complex networks such as peer-to-peer networks and social networks. It should be noted that this book presents a new horizon for future research based on random walk and learning systems. The rationale behind the proposed models can be extended with other learning techniques such as Q-learning. In the other hand, there are numerous versions of random walk which should be reconfigured with learning methods in real-world applications. In this chapter, the conclusions and the future directions are explained in more detail.