  We present a new approach for identifying situations and behaviours, which we
call "moves", from soccer games in the 2D simulation league. Being able to
identify key situations and behaviours are useful capabilities for analysing
soccer matches, anticipating opponent behaviours to aid selection of
appropriate tactics, and also as a prerequisite for automatic learning of
behaviours and policies. To support a wide set of strategies, our goal is to
identify situations from data, in an unsupervised way without making use of
pre-defined soccer specific concepts such as "pass" or "dribble". The recurrent
neural networks we use in our approach act as a high-dimensional projection of
the recent history of a situation on the field. Similar situations, i.e., with
similar histories, are found by clustering of network states. The same networks
are also used to learn so-called conceptors, that are lower-dimensional
manifolds that describe trajectories through a high-dimensional state space
that enable situation-specific predictions from the same neural network. With
the proposed approach, we can segment games into sequences of situations that
are learnt in an unsupervised way, and learn conceptors that are useful for the
prediction of the near future of the respective situation.
