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for the distributed learning process between the agent
and the human experts. This will propose a complete
concept of parallel learning. We are also planning to
optimise the learning data and dig deeper to explore
the nature of the prediction accuracy, and it is rele-
vant to the available data. Although our model intro-
duces faults tolerance and communication failure or
reduction, a comparative analysis with other systems
in (Makonin et al., 2016; Tecuci et al., 2007) using
real agents marked as future work. We will also look
at how the model and the predictions tools act in a
highly changing environment.
ACKNOWLEDGEMENTS
We appreciate the effort of Petroleum Technology
Trust Funds (PTDF) of Nigeria for sponsoring this
project.
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