Learning regions for building a world model from clusters in probability distributions

Abstract—A developing agent learns a model of the world
by observing regularities occurring in its sensory inputs. In a
continuous domain where the model is represented by a set of
rules, a significant part of the task of learning such a model is to
find appropriate intervals within the continuous state variables,
such that these intervals can be used to define rules whose
predictions are reliable. We propose a technique to find such
intervals (or regions) by means of finding clusters on approximate
probability distributions of sensory variables. We compare
this cluster-based method with an alternative landmark-based
algorithm. We evaluate both techniques on a data log recorded
in a simulation based on OpenArena, a three-dimensional firstperson-
perspective computer game, and demonstrate the results
of how the techniques can learn rules which describe walking
behaviour. While both techniques work reasonably well, the
clustering approach seems to give more “natural” regions which
correspond more closely to what a human would expect; we
speculate that such regions should be more useful if they are to
form a basis for further learning of higher order rules.