Transfer of Learned Knowledge in
Life-Long Learning Agents
Joseph O'Sullivan, Carnegie Mellon University
Previous work has demonstrated that the performance of machine learning
algorithms can be improved by exploiting various forms of knowledge, such
as domain theories. More recently, it has been recognized that some
forms of knowledge can in turn be learned -- in particular, action models
and task-specific internal representations. Using learned knowledge as a
source of learning improvement can be particularly appropriate for agents
that face many tasks. Over a long lifetime, an agent can amortize effort
expended in learning knowledge by reducing the number of examples
required to learn further tasks. In developing such a ``life-long
learning'' agent, a number of research issues arise, including: will an
agent benefit from learned knowledge, can an agent exploit multiple
sources of learned knowledge, how should the agent adapt as a new task
arrives, how might the order of task arrival impact learning, and how can
such an agent be built?
I propose that an agent can be constructed which learns knowledge and
exploits that knowledge to effectively improve further learning by
reducing the number of examples required to learn. I intend to study the
transfer of learned knowledge by life-long learning agents within a
neural network based architecture capable of increasing capacity with the
number of tasks faced. This proposal describes an appropriate
architecture, based on preliminary work in controlled settings. This work
has shown that learned knowledge can reduce the number of examples
required to learn novel tasks and that combining previously separate
mechanisms can yield a synergistic improvement on learning ability. It
has also explored how capacity can be expanded as new tasks arise over
time and how the order in which tasks arise can be exploited with a
graded curriculum. This preliminary work will be applied to a life-long
learning agent and extended by carrying out experimental studies of a
simulated robot agent in a controlled environment and of a real-world
mobile robot agent in Wean Hall.
Postscript
Joseph Kieran O'Sullivan
Last modified: Mon Aug 25 20:54:59 EDT 1997