Computer Science Department
School of Computer Science, Carnegie Mellon University
A Probabilistic Language based upon Sampling Functions
Sungwoo Park, Frank Pfenning, Sebastian Thrun*
Keywords: Probabilistic language, probability distribution,
sampling function, robotics
As probabilistic computations play an increasing role
in solving various problems, researchers have designed probabilistic languages
that treat probability distributions as primitive datatypes. Most
probabilistic languages, however, focus only on discrete distributions
and have limited expressive power. In this paper, we present a
probabilistic language, called λO, which uniformly
supports all kinds of probability distributions -- discrete distributions,
continuous distributions, and even those belonging to neither group.
Its mathematical basis is sampling functions, i.e.,
mappings from the unit interval (0.0,1.0] to probability domains.
We also briefly describe the implementation of λO
as an extension of Objective CAML and demonstrate its practicality with three
applications in robotics: robot localization, people tracking,
and robotic mapping. All experiments have been carried out
with real robots.
*Computer Science Department, Stanford University