A Fixed-Structure Learning Automaton Solution to the Stochastic Static Mapping Problem
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 18 - Volume 19
Engineering Applications of Artificial Intelligence
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
Solving multiconstraint assignment problems using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Stochastic point location in non-stationary environments and its applications
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
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
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
Service selection in stochastic environments: a learning-automaton based solution
Applied Intelligence
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
Information Sciences: an International Journal
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
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The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry (1986). The pioneering work of these authors was the pursuit algorithm, which pursues only the current estimated optimal action. If this action is not the one with the minimum penalty probability, this algorithm pursues a wrong action. In this paper, we argue that a pursuit scheme that generalizes the traditional pursuit algorithm by pursuing all the actions with higher reward estimates than the chosen action, minimizes the probability of pursuing a wrong action, and is a faster converging scheme. To attest this, we present two new generalized pursuit algorithms (GPAs) and also present a quantitative comparison of their performance against the existing pursuit algorithms. Empirically, the algorithms proposed here are among the fastest reported LA to date.