  The review analyzes the fundamental principles which Artificial Intelligence
should be based on in order to imitate the realistic process of taking
decisions by humans experiencing emotions. Two approaches are compared, one
based on quantum theory and the other employing classical terms. Both these
approaches have a number of similarities, being principally probabilistic. The
analogies between quantum measurements under intrinsic noise and affective
decision making are elucidated. It is shown that cognitive processes have many
features that are formally similar to quantum measurements. This, however, in
no way means that for the imitation of human decision making Affective
Artificial Intelligence has necessarily to rely on the functioning of quantum
systems. Appreciating the common features between quantum measurements and
decision making helps for the formulation of an axiomatic approach employing
only classical notions. Artificial Intelligence, following this approach,
operates similarly to humans, by taking into account the utility of the
considered alternatives as well as their emotional attractiveness. Affective
Artificial Intelligence, whose operation takes account of the cognition-emotion
duality, avoids numerous behavioural paradoxes of traditional decision making.
A society of intelligent agents, interacting through the repeated multistep
exchange of information, forms a network accomplishing dynamic decision making.
The considered intelligent networks can characterize the operation of either a
human society of affective decision makers, or the brain composed of neurons,
or a typical probabilistic network of an artificial intelligence.
