Creating complex and realistic group behaviors
can be a difficult and time consuming task.
Automated approaches for motion generation typically involve
explicitly defining a set of possible agent behaviors, associating
appropriate behaviors with all environmental events, and setting
the priorities among various behaviors in every possible situation.
Generally, such approaches are pre-tuned to particular situations and
are difficult to adapt for other scenarios or for different sets of
In this paper, we investigate methods to facilitate the generation of
complex group behaviors for applications such as games, virtual reality,
robotics and biological/ecological simulation.
Our general approach is to provide a framework that
automatically combines simple composable behaviors into
more complex behaviors. Adaptation to new environments and
specialization for new tasks or new agent abilities is supported
by a ``learning'' process through which agents select their
current behavior based on their prior experiences.
We illustrate how our framework can be applied to
pursuit/evasion and laser tag scenarios.