  This article introduces both a new algorithm for reconstructing
epsilon-machines from data, as well as the decisional states. These are defined
as the internal states of a system that lead to the same decision, based on a
user-provided utility or pay-off function. The utility function encodes some a
priori knowledge external to the system, it quantifies how bad it is to make
mistakes. The intrinsic underlying structure of the system is modeled by an
epsilon-machine and its causal states. The decisional states form a partition
of the lower-level causal states that is defined according to the higher-level
user's knowledge. In a complex systems perspective, the decisional states are
thus the "emerging" patterns corresponding to the utility function. The
transitions between these decisional states correspond to events that lead to a
change of decision. The new REMAPF algorithm estimates both the epsilon-machine
and the decisional states from data. Application examples are given for hidden
model reconstruction, cellular automata filtering, and edge detection in
images.
