  After the development of different machine learning and manifold learning
algorithms, it may be a good time to put them together to make a powerful mind
for machine. In this work, we propose affective manifolds as components of a
machine's mind. Every affective manifold models a characteristic group of mind
and contains multiple states. We define the machine's mind as a set of
affective manifolds. We use a learning model for mapping the input signals to
the embedding space of affective manifold. Using this mapping, a machine or a
robot takes an input signal and can react emotionally to it. We use deep metric
learning, with Siamese network, and propose a loss function for affective
manifold learning. We define margins between states based on the psychological
and philosophical studies. Using triplets of instances, we train the network to
minimize the variance of every state and have the desired distances between
states. We show that affective manifolds can have various applications for
machine-machine and human-machine interactions. Some simulations are also
provided for verification of the proposed method. It is possible to have as
many affective manifolds as required in machine's mind. More affective
manifolds in the machine's mind can make it more realistic and effective. This
paper opens the door; we invite the researchers from various fields of science
to propose more affective manifolds to be inserted in machine's mind.
