4555 Overlook Ave., S.W.
Washignton, DC 20375
phone: (202) 404-4939
fax: (202) 767-3172
Principal Investigator's publication
list (coming soon)
We are applying
computational models of evolution to the creation of software
control systems for coordinated teams of robots. The goal
is to create intelligent, dynamically autonomous systems.
Our current focus is on a variety of open issues, including
characterizing the power and tradeoffs associated with alternative
behavioral representations, bridging the gap between simulation
and the real world with continuous and embedded learning,
and the application of coevolution to morphology as well
apply evolutionary algorithms to the creation of behaviors
for teams of intelligent, dynamically autonomous robots,
we must develop effective techniques for evolving solutions
in the form of interacting coadapted components. We have
developed a general architecture for evolving such components
as a collection of two or more genetically-isolated species
that interact and adapt to one another within a shared domain
model. He are interested in harnessing the game-theoretic
power of coevolution to learn team behaviors, while at the
same time understanding the effects certain key design choices
and problem properties have on successful performance.
control systems for coordinated teams of autonomous robots
will require suitable behavioral representations. These
can be difficult to design since they must be both specialized
and easily evolvable. Existing theoretical work in evolutionary
biology may help provide us with a principled way of designing
specialized representations and reproductive operators.
By building on this work, we intend to develop principles
for designing representations and operators for the next
generation of evolutionary algorithms.
and Embedded Learning (Anytime Learning)
autonomy will require systems that can adapt to changes
in the environment and to changes in their own capabilities,
as well as to changes within the team in which they are
a member. We are investigating several hybrid machine learning
techniques, and various representations, as well as designing
a system that allows for safely learning and adapting while
in the real world.