  Collective motion is found in various animal systems, active suspensions and
robotic or virtual agents. This is often understood using high level models
that directly encode selected empirical features, such as co-alignment and
cohesion. Can these features be shown to emerge from an underlying, low-level
principle? We find that they emerge naturally under Future State Maximisation
(FSM). Here agents perceive a visual representation of the world around them,
such as might be recorded on a simple retina, and then move to maximise the
number of different visual environments that they expect to be able to access
in the future. Such a control principle may confer evolutionary fitness in an
uncertain world by enabling agents to deal with a wide variety of future
scenarios. The collective dynamics that spontaneously emerge under FSM resemble
animal systems in several qualitative aspects, including cohesion, co-alignment
and collision suppression, none of which are explicitly encoded in the model. A
multi-layered neural network trained on simulated trajectories is shown to
represent a heuristic mimicking FSM. Similar levels of reasoning would seem to
be accessible under animal cognition, demonstrating a possible route to the
emergence of collective motion in social animals directly from the control
principle underlying FSM. Such models may also be good candidates for encoding
into possible future realisations of artificial "intelligent" matter, able to
sense light, process information and move.
