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Matthew E. Taylor, Cynthia
Matuszek, Pace Reagan Smith, and Michael Witbrock. Guiding Inference
with Policy Search Reinforcement Learning. In The Twentieth International FLAIRS Conference, May 2007.
FLAIRS-2007
[PDF]138.5kB [postscript]266.6kB
Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily slow as complexity and ground facts grow. As automated approaches to ontology-building become more prevalent and sophisticated, knowledge base systems become larger and more complex, necessitating techniques for faster inference. This work uses reinforcement learning, a statistical machine learning technique, to learn control laws which guide inference. We implement our learning method in ResearchCyc, a very large knowledge base with millions of assertions. A large set of test queries, some of which require tens of thousands of inference steps to answer, can be answered faster after training over an independent set of training queries. Furthermore, this learned inference module outperforms ResearchCyc's integrated inference module, a module that has been hand-tuned with considerable effort.
@InProceedings{FLAIRS07-taylor-inference,
author="Matthew E.\ Taylor and Cynthia Matuszek and Pace Reagan Smith and Michael Witbrock",
title="Guiding Inference with Policy Search Reinforcement Learning",
booktitle="The Twentieth International FLAIRS Conference",
month="May",year="2007",
abstract="Symbolic reasoning is a well understood and
effective approach to handling reasoning over
formally represented knowledge; however, simple
symbolic inference systems necessarily slow as
complexity and ground facts grow. As automated
approaches to ontology-building become more
prevalent and sophisticated, knowledge base systems
become larger and more complex, necessitating
techniques for faster inference. This work uses
reinforcement learning, a statistical machine
learning technique, to learn control laws which
guide inference. We implement our learning method in
ResearchCyc, a very large knowledge base with
millions of assertions. A large set of test queries,
some of which require tens of thousands of inference
steps to answer, can be answered faster after
training over an independent set of training
queries. Furthermore, this learned inference module
outperforms ResearchCyc's integrated inference
module, a module that has been hand-tuned with
considerable effort.",
wwwnote={<a href="http://www.cise.ufl.edu/~ddd/FLAIRS/flairs2007/">FLAIRS-2007</a>},
}
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