  We describe the design and implementation of an autonomous adaptive software
agent that addresses the practical problem of observing undersampled, periodic,
time-varying phenomena using a network of HTN-compliant robotic telescopes. The
algorithm governing the behaviour of the agent uses an optimal geometric
sampling technique to cover the period range of interest, but additionally
implements proactive behaviour that maximises the optimality of the dataset in
the face of an uncertain and changing operating environment.
