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

@article{Zhang2013OnIT,
  title={On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata},
  author={Xuan Zhang and Ole-Christoffer Granmo and B. John Oommen},
  journal={Applied Intelligence},
  year={2013},
  volume={39},
  pages={782-792}
}
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 second involves discretizing the probability space in which the LA operates. This paper demonstrates how both of these can be simultaneously utilized, and in particular, by using the family of Bayesian estimates that have been proven to have distinct advantages over their… CONTINUE READING