  We propose the concept of intelligent middle-level game control, which lies
on a continuum of control abstraction levels between the following two dual
opposites: 1) high-level control that translates player's simple commands into
complex actions (such as pressing Space key for jumping), and 2) low-level
control which simulates real-life complexities by directly manipulating, e.g.,
joint rotations of the character as it is done in the runner game QWOP. We
posit that various novel control abstractions can be explored using recent
advances in movement intelligence of game characters. We demonstrate this
through design and evaluation of a novel 2-player martial arts game prototype.
In this game, each player guides a simulated humanoid character by clicking and
dragging body parts. This defines the cost function for an online continuous
control algorithm that executes the requested movement. Our control algorithm
uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in a rolling
horizon manner with custom population seeding techniques. Our playtesting data
indicates that intelligent middle-level control results in producing novel and
innovative gameplay without frustrating interface complexities.

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