Seminar on Computational Learning and Adaptation
Symbolic Function Approximation for Control
Controlling physical and simulated physical systems, such as an
airplane or an automobile, generally takes the form of using a
response function to compute control adjustments based on the current
state of the enviroment and the desired state. These systems are
frequently complex and difficult to control, requiring much specific,
detailed knowledge. An approach to accurately controlling these
systems is to enable the system to autonomously approximate the ideal
response function through experiencing the results of its own actions.
Although most work along these lines uses strictly numerical
regression techniques for minimizing error, our work utilizes a
symbolic high-level representation. Applying symbolic techniques to
learning control of physical systems allows reasoning at a high level
of abstraction and tight integration with other symbolic capabilities,
such as natural language and planning. SPLICE (Symbolic Performance
& Learning In Continuous-valued Environments) is a symbolic agent for
function approximation and control implemented in the Soar
architecture. SPLICE uses a three-level framework to first classify
its sensory information into symbolic regions, then map the set of
regions to a local model, then use the local model to determine an
action. The agent monitors the results of the action and
incrementally learns by changing its action mapping and local models.
Over time, the models gradually become more specific and accurate.
SPLICE performs comparably to other function approximation algorithms
in a variety of domains. The final goal is to create an effective
controller and improve our understanding of symbolic processing for
complex physical systems.
Date: Weds., Mar. 12 Time: 4:15-5:30PM Place: Gates 100
The goal of this seminar is to increase
communication among local researchers with interests in computational
approaches to learning and adaptation. If you would like to be added
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