Abstract
Random walk algorithms are used to problem-solving, modeling, and simulation in many types of networks including computer networks, social networks, and biological networks. In real-world problems, the non-intelligent models of random walk may not be used as a problem-solving method. Recently, intelligent models of random walk have been reported in the literature. These models try to extend the basic versions of random walk to design a novel problem-solving method. The learning mechanism of these models is based on learning automata. In these models, the design of feedback systems given by the theory of learning automata is used to design intelligent models of random walk. In this chapter, we discuss about the weaknesses of non-intelligent models of random walk as a problem-solving method in real-world applications. We also give the required information about random walk algorithms and the theory of learning automata.
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Saghiri, A.M., Khomami, M.D., Meybodi, M.R. (2019). Random Walk Algorithms: Definitions, Weaknesses, and Learning Automata-Based Approach. In: Intelligent Random Walk: An Approach Based on Learning Automata. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-030-10883-0_1
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DOI: https://doi.org/10.1007/978-3-030-10883-0_1
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