This page is defunct. It exists to help citeseer find some
For a complete listing of Ric Crabbe's Publications, go to his Bio.
Carlos Antonio Acosta Calderon and Huosheng HuRequirements
for Getting a Robot to Grow up In the proceedings of 7th European
Conference on Artificial Life (ECAL 2003), Dortmund, Germany,
September 14-17, 2003
P. Gůmez, A. Alvarez, R. MartŪnez, M. Pťrez, V. Rodellar and
V. Nieto A
DSP-based modular architecture for Noise Cancellation and Speech
Recognition. 1998 IEEE Int. Symposium on Circuits and
Systems, ISCAS'98 Monterey, California, USA, 31-May/3-June, 1998
Jens Wawerla, Gaurav S. Sukhatme, Maja J. Mataric, Collective
Construction with Multiple Robots Proceedings of the IEEE/RSJ
International Conference on Intelligent Robots and Systems (IROS 2002)
October 4, 2002.
M. McTear, Spoken
dialogue technology: enabling the conversational user interface
ACM Computing Surveys (CSUR), 34:1, 2002.
Bill Manaris, Natural Language Processing: A Human-Computer
Interaction Perspective, In Advances in Computers (Marvin
V. Zelkowitz, ed.), vol. 47, pp. 1-66, Academic Press, New York, 1998.
C.A. Acosta Calderon and H. Hu,
Robotics Societies: Elements of
Learning by Imitation, Proceedings of the 21st IASTED Int. Conference
on Applied Informatics, pp. 315-320, Innsbruck, Austria, 10-13
Jeanne Fromer, Learning Optimal Discourse Strategies in a Spoken
Dialogue Systems (1998) (unpublished S.M. thesis, Massachusetts
Institute of Technology) (on file with the Massachusetts Institute of
Stoness, Scott C. Continuous Understanding: A First Look at CAFE,
Area Paper for URCS, May 2001
Niels Ole Bernsen, Hans Dybkjśr and Laila Dybkjśr: Elements of Speech Interaction. In Dybkjśr, L. (Ed.): Proceedings of the Third Spoken Language Dialogue and Discourse Workshop, Vienna, September 1997.
We are developing agents that can build structures out of building materials
found in a two dimensional environment consisting of circular objects called
discs. Primarily we focus on nests, which are structures that can protect
the agents from predators or other dangers like weather. Nests include
briar patches and enclosures. Briar patches are collections of material
packed close together, preventing passage of predators but not close enough
to prevent passage of the agents, and enclosures are surrounding walls
with one or more openings. The agents, controlled by artificial neural
networks, must complete the task while performing other tasks necessary
to their survival. As the agents build the object, they must still be able
satisfy their more immediate goals, such as eating, or fleeing predators.
Current research focuses
on learning behaviors and building maps of the environment. The agents
use a new type reinforcement learning to build sparesely connected neural
networks. The also can learn vicariously, that is learn about their environment
by observing the results of actions done by peers. Mapping is currently
perfromed with concentric rings of neurons that activate
The experiments are run in DiscoTech, a continuous two-dimensional world.
Everything in the environment (both objects and agents) are circular discs.
Each disc is a single color: agents are black, while the passive discs
consist of green-colored food discs, blue-colored water discs and building
discs of a variety of other colors. Food and water discs can be consumed
by the agents. Food discs and water disappear when eaten or drunk.
Agent effectors consist of: turning left or right with some movement
forward (i.e. an agent cannot spin in place); moving forward, left, right
or backwards; eating; drinking; painting discs (i.e. changing the color
of a non-food or non-water disc); and grasping. Grasping must be done continuously
over a period of time for an agent to hold onto a disc for that period
(Dropping is done as a cessation of grasping).
Time is divided into discrete time-steps. At each time-step, every agent
is given in parallel an opportunity to act. Each agent returns a set of
actions, all of which are carried out in parallel, e.g. an agent can simultaneously
execute a grasp, move, and turn. The size of a time-step in the world is
roughly analogous to a fraction of a second. The farthest an agent can
move in a single time-step is 4 units, or one third of its diameter. Typically,
agents move around 1 unit per time-step.
The simulator for the Discotech environment was written in Common Lisp,
and is run on Pentuim II machines running the Linux Operating system in
the GNU Common Lisp environment. The graphical display is run in a separate
process, written in Java.
Who's who in the DiscoTech
Manager on Duty: Frederick Crabbe,
General Manager: Michael
Dyer, Professor, UCLA
DJs: Position Open
Bartenders: Anand Panangadan and Gerald Chao, Graduate Students, UCLA
Tech Reports and Other Publications
Chao G., Panangadan, A. , and Dyer, M.G. Learning
to Integrate Reactive and Planning Behaviors for Construction.
in the proceedings of the Sixth International Conference on Simulation
of Adaptive Behavior (SAB), 2000.
Chao, G. and Dyer M. G., Concentric
Maps for Neural Network Based Navigation UCLA-TR #990016.
Crabbe, F. and Dyer, M., MAXSON:
Max-Based Second-Order Neural Network Reinforcement Learner for Mobile
Agents in Continuous Environments. UCLA-TR #990009.
Crabbe, F. and Dyer, M., Goal
Sequence Achievement Using Higher-Order Neural Connections in Construction
Agents. UCLA-TR #980018.
Crabbe, F. and Dyer, M., Cooperative
Behavior, Communication and Task Decomposition in Protective Structure
Construction by Neurally Controlled Agents. UCLA-TR #980019.
This work is supported in part by an Intel University Research Program
grant to my advisor, Michael Dyer. The Pentium II machines were also donated
by Intel. Microsoft has donated several pieces of software.
If you're interested in this sort of stuff, here are some other links: