An adaptive call admission algorithm for cellular networks
Computers and Electrical Engineering
Learning Automata Based Intelligent Tutorial-like System
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Modeling a student-classroom interaction in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On using learning automata to model a student's behavior in a tutorial-like system
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Modeling a student's behavior in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling a domain in a tutorial-like system using learning automata
Acta Cybernetica
The Bayesian pursuit algorithm: a new family of estimator learning automata
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Learning behaviors of the hierarchical structure stochastic automata
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
A stochastic search on the line-based solution to discretized estimation
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
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A learning automaton (LA) is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata (LAs) have been proposed, with the class of estimator algorithms being among the fastest ones, Thathachar and Sastry, through the pursuit algorithm, introduced the concept of learning algorithms that pursue the current optimal action, following a reward-penalty learning philosophy. Later, Oommen and Lanctot extended the pursuit algorithm into the discretized world by presenting the discretized pursuit algorithm, based on a reward-inaction learning philosophy. In this paper we argue that the reward-penalty and reward-inaction learning paradigms in conjunction with the continuous and discrete models of computation, lead to four versions of pursuit learning automata. We contend that a scheme that merges the pursuit concept with the most recent response of the environment, permits the algorithm to utilize the LAs long-term and short-term perspectives of the environment. In this paper, we present all four resultant pursuit algorithms, prove the E-optimality of the newly introduced algorithms, and present a quantitative comparison between them