UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the
240-node cluster maya that will be available after Summer 2014.
Currently, the 84-node cluster tara still operates independently,
until it becomes part of maya at the end of Summer 2014.
Please see the 2013 Resources Pages under the Resources tab for tara information.
Marie desJardins and James MacGlashan, Department of Computer Science
and Electrical Engineering
Intelligent agents are often given a set of abilities by a designer
that the agent can use to accomplish various goals. However, if an
agent is presented with a new complex problem that requires high-level
abilities that a designer has not defined for it, then the agent may not
be able to solve the problem in a reasonable amount of time. This makes
agents very dependent on the designer to define an appropriate set of
skills to use. Humans, in contrast, are born with very low-level
abilities, and learn high-level reactive skills over time. In addition
to learning these skills, humans can plan solutions to problems using
the learned skills, allowing them to quickly find solutions to new
problems. AI techniques, on the other hand, often use either a pure
learning approach or a pure planning approach, which each have their own
advantages and disadvantages.
Skill Bootstrapping is a novel integrated planning and learning
framework that will allow an agent to start with primitive actions, and
progressively learn generalized reactive skills that can be used during
planning to quickly solve new, more complex, problems.