Teaching a new dog old tricks - the Assumption Architecture
A rule-based training system for simple LEGO robots
Abstract:
We present a learning system intended for the simplistic ecological
niche of LEGO robots.
Binary feedback in the form of good! and
bad! from a trainer is enough for the robot to learn a variety of
simple tasks. The robot's sensors are structured in groups called
senses that pre-process sensor data into useful information. The
robot forms consistent assumptions about when to take what action. All
feedback is considered significant and may have great impact on the
learned assumptions; this is one of the reasons for fast
learning. Many experiments were done in simulation, one also with a
real LEGO robot.
See the (Word format) paper,
accepted for the workshop "Learning'00".