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.
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