Applications and Future Research Directions of Learning Automata Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.1002/9781394188536.ch8
· OA: W4388783166
As a powerful computational intelligence algorithm, Learning Automata (LAs) have been studied and applied widely. In this chapter, their applications and future research directions are discussed. Researchers have developed many algorithm variants of LAs to meet different application requirements. Thus LAs can be applied to real-world problems such as classification, clustering, games, knapsack problems, decision problems in networks, optimization, LA parallelization, design ranking, and scheduling. Classification is the main part of supervised learning, and Support Vector Machine (SVM) is one of the most widely applied algorithms in the classification area. Similar to classification, clustering of data has been an important research content for a long time in machine learning research field. LA learns the optimal action based on the interaction between intelligent agentsand random environments. The field of LA has been studied and analyzed extensively for many years.
