By "intelligently" locating a sensor with respect to its environment it is possible
to minimize the number of sensing operations required to perform many tasks.
This is particularly important for sensing media which provide only "sparse" data,
such as tactile sensors and sonar. In this thesis, a system is described which uses
the principles of statistical decision theory to determine the optimal sensing locations
to perform recognition and localization operations. The system uses a
Bayesian approach to utilize any prior object information (including object models
or previously-acquired sensory data) in choosing the sensing locations.</p