  Remote science operations require automated systems that can both act and
react with minimal human intervention. One such vision is that of an
intelligent instrument that collects data in an automated fashion, and based on
what it learns, decides which new measurements to take. This innovation
implements experimental design and unites it with data analysis in such a way
that it completes the cycle of learning. This cycle is the basis of the
Scientific Method.
  The three basic steps of this cycle are hypothesis generation, inquiry, and
inference. Hypothesis generation is implemented by artificially supplying the
instrument with a parameterized set of possible hypotheses that might be used
to describe the physical system. The act of inquiry is handled by an inquiry
engine that relies on Bayesian adaptive exploration where the optimal
experiment is chosen as the one which maximizes the expected information gain.
The inference engine is implemented using the nested sampling algorithm, which
provides the inquiry engine with a set of posterior samples from which the
expected information gain can be estimated. With these computational structures
in place, the instrument will refine its hypotheses, and repeat the learning
cycle by taking measurements until the system under study is described within a
pre-specified tolerance. We will demonstrate our first attempts toward
achieving this goal with an intelligent instrument constructed using the LEGO
MINDSTORMS NXT robotics platform.
