  Devising a strategy to make a system mimicking behaviors from another system
is a problem that naturally arises in many areas of Computer Science. In this
work, we interpret this problem in the context of intelligent agents, from the
perspective of LTLf, a formalism commonly used in AI for expressing
finite-trace properties. Our model consists of two separated dynamic domains,
D_A and D_B, and an LTLf specification that formalizes the notion of mimicking
by mapping properties on behaviors (traces) of D_A into properties on behaviors
of D_B. The goal is to synthesize a strategy that step-by-step maps every
behavior of D_A into a behavior of D_B so that the specification is met. We
consider several forms of mapping specifications, ranging from simple ones to
full LTLf, and for each we study synthesis algorithms and computational
properties.
