This video about QLAP was accepted to the 2010
AAAI video competition. It won the award for Best Educational Video!
An agent, human or otherwise, receives a large sensory stream
from the continuous world that must be broken up into useful features. The agent
must also learn to use its low-level effectors to bring about desired changes in the
Humans and other animals have adapted to their environment through a combination of evolution
and individual learning. We blur the distinction between individual and species learning
and define the problem abstractly as how can an agent from low-level sensors and effectors
learn high-level states and actions through autonomous experience with the environment.
Pierce and Kuipers 
have shown that an agent can learn the structure of its sensory and motor apparatus.
Building on this work, Modayil and Kuipers  have shown
how an agent can individuate and track objects in its sensory stream.
Our approach builds on this work to enable an agent to learn a discrete sensory description
and a hierarchical set of actions.
We call our approach the Qualitative
Learner of Action and Perception, QLAP.
QLAP learns a discretization of the environment and predictive models of the dynamics of the
environment as shown in Figure 1.
QLAP assumes that the sensory stream (Fig. 1-a) is converted
(Fig. 1-b) to a set of continuous variables. These variables give
the locations of objects and distances between them.
To build models of the environment, QLAP must learn the necessary discretization.
QLAP begins with a very simple discretization (Fig. 1-c), that
essentially can only give the direction of movement of objects and
if a distance between objects is increasing or decreasing.
From this low-resolution representation, QLAP learns (Fig. 1-d) a set of
to describe the dynamics of the environment. These initial models are simple and unreliable,
but they make predictions about changes in the environment
that can be used to generate a supervised learning signal.
This learning signal points QLAP towards new discretizations that
can make the models more reliable (Fig. 1-e). Through this synergy of
discretization and model building, QLAP builds an increasingly sophisticated representation of the