Markov chains and linear model-based hybrid prediction algorithm for cognitive agents
by Smail Tigani; Mouhamed Ouzzif; Abderrahim Hasbi
International Journal of Intelligent Systems Design and Computing (IJISDC), Vol. 1, No. 1/2, 2017

Abstract: The aim of this work is the improvement of cognitive agents performance. An agent is designed to follow fixed instructions to reach a given goal, this can be considered a limitation of agent technology because it does not have a minimum level of intelligence. This work proposes a new algorithm able to make prediction and learn from its experience in the prediction of a supervised environment. This allows the agent to analyse the history observations and make prediction of future environment state using the designed auto-adaptive algorithm based on stochastic models. The algorithms designed in this work can be applied in optimised scheduling or random environments management.

Online publication date: Tue, 14-Mar-2017

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