  A robot's actions are inherently stochastic, as its sensors are noisy and its
actions do not always have the intended effects. For this reason, the agent
language Golog has been extended to models with degrees of belief and
stochastic actions. While this allows more precise robot models, the resulting
programs are much harder to comprehend, because they need to deal with the
noise, e.g., by looping until some desired state has been reached with
certainty, and because the resulting action traces consist of a large number of
actions cluttered with sensor noise. To alleviate these issues, we propose to
use abstraction. We define a high-level and nonstochastic model of the robot
and then map the high-level model into the lower-level stochastic model. The
resulting programs are much easier to understand, often do not require belief
operators or loops, and produce much shorter action traces.
