Browsing Faculty of Engineering and Science by Subject "learning automata"

Now showing items 1-4 of 4

    • A hierarchical learning scheme for solving the Stochastic Point Location problem 

      Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John; Goodwin, Morten (Lecture Notes in Computer Science;7345, Chapter; Peer reviewed, 2012)
      This paper deals with the Stochastic-Point Location (SPL) problem. It presents a solution which is novel in both philosophy and strategy to all the reported related learning algorithms. The SPL problem concerns the task ...

    • A Stochastic Search on the Line-Based Solution to Discretized Estimation 

      Yazidi, Anis; Granmo, Ole-Christoffer; Oommen, B. John (Lecture Notes in Computer Science;7345, Chapter; Peer reviewed, 2012)
      Recently, Oommen and Rueda [11] presented a strategy by which the parameters of a binomial/multinomial distribution can be estimated when the underlying distribution is nonstationary. The method has been referred to as the ...

    • Discretized Bayesian pursuit – A new scheme for reinforcement learning 

      Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, B. John (Lecture Notes in Computer Science;7345, Chapter; Peer reviewed, 2012)
      The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability ...

    • On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata 

      Zhang, Xuan; Granmo, Ole-Christoffer; Oommen, B. John (Journal article; Peer reviewed, 2013)
      There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the ...