The aim of the article is to show a stochastic approach for both modelling and optimizing the statistical agent belief in a probability model.
Two networks are defined: a decision network
$\mathfrak{D}$
of the agent belief state and a utility network
$\mathfrak{U}$
, presenting the utility structure of the agent belief problem.
The agent belief is presented via the following three items (
$\mathfrak{B},\mathfrak{D},\mathfrak{U}$
), where
$\mathfrak{B}$
is a Bayesian network, presenting the probability structure of the agent belief problem.
Two propagation algorithms in
$\mathfrak{D}$
and in
$\mathfrak{U}$
are also presented.
