Add to ORKGIn many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence prope...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Learning automata which update their action probabilities on the basis of the responses they get fro...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
The problem of a stochastic learning automation interacting with an unknown random environment is co...
A learning automation is a finite state machine which learns the optimal action from a set of action...
Abstract:- A stochastic automaton can perform a finite number of actions in a random environment. Wh...
Abstract—A learning automaton (LA) is an automaton that interacts with a random environment, having ...
This paper presents an overview of the field of Stochastic Learning Automata (LA), and concentrates,...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Learning automata are considered which update their action probabilities on the basis of the respons...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Learning automata which update their action probabilities on the basis of the responses they get fro...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
In many problems of decision making under uncertainty the system has to acquire knowledge of its env...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
We consider stochastic automata models of learning systems in this article. Such learning automata s...
The problem of a stochastic learning automation interacting with an unknown random environment is co...
A learning automation is a finite state machine which learns the optimal action from a set of action...
Abstract:- A stochastic automaton can perform a finite number of actions in a random environment. Wh...
Abstract—A learning automaton (LA) is an automaton that interacts with a random environment, having ...
This paper presents an overview of the field of Stochastic Learning Automata (LA), and concentrates,...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Learning automata are considered which update their action probabilities on the basis of the respons...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Learning automata which update their action probabilities on the basis of the responses they get fro...