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<title>Theory Refinement</title>

<h1>Theory Refinement</h1>

To view a paper, click on the open book image. <br> 
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<a name="rapture-dissertation-96.ps.Z"</a>

<b><li>Combining Symbolic and Connectionist Learning Methods to Refine
Certainty-Factor Rule-Bases<br></b>
J. Jeffrey Mahoney<br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, May, 1996.<p>

<blockquote>
This research describes the system RAPTURE, which is designed to
revise rule bases expressed in certainty-factor format.  Recent
studies have shown that learning is facilitated when biased with
domain-specific expertise, and have also shown that many real-world
domains require some form of probabilistic or uncertain reasoning in
order to successfully represent target concepts. RAPTURE was designed
to take advantage of both of these results. <p>

Beginning with a set of certainty-factor rules, along with
accurately-labelled training examples, RAPTURE makes use of both
symbolic and connectionist learning techniques for revising the rules,
in order that they correctly classify all of the training examples. A
modified version of backpropagation is used to adjust the certainty
factors of the rules, ID3's information-gain heuristic is used to add
new rules, and the Upstart algorithm is used to create new hidden
terms in the rule base. <p>

Results on refining four real-world rule bases are presented that
demonstrate the effectiveness of this combined approach.  Two of these
rule bases were designed to identify particular areas in strands of
DNA, one is for identifying infectious diseases, and the fourth
attempts to diagnose soybean diseases.  The results of RAPTURE are
compared with those of backpropagation, C4.5, KBANN, and other
learning systems.  RAPTURE generally produces sets of rules that are
more accurate that these other systems, often creating smaller sets of
rules and using less training time. <p>

</blockquote>

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<a name="banner-proposal-95.ps.Z"</a>

<b><li> Refinement of Bayesian Networks by Combining Connectionist and
Symbolic Techniques <br></b>
Sowmya Ramanchandran<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1995. <p>

<blockquote>
Bayesian networks provide a mathematically sound formalism for
representing and reasoning with uncertain knowledge and are as such
widely used. However, acquiring and capturing knowledge in this
framework is difficult. There is a growing interest in formulating
techniques for learning Bayesian networks inductively. While the
problem of learning a Bayesian network, given complete data, has been
explored in some depth, the problem of learning networks with
unobserved causes is still open. In this proposal, we view this
problem from the perspective of theory revision and present a novel
approach which adapts techniques developed for revising theories in
symbolic and connectionist representations.  Thus, we assume that the
learner is given an initial approximate network (usually obtained from
a expert). Our technique inductively revises the network to fit the
data better.  Our proposed system has two components: one component
revises the parameters of a Bayesian network of known structure, and
the other component revises the structure of the network. The
component for parameter revision maps the given Bayesian network into
a multi-layer feedforward neural network, with the parameters mapped
to weights in the neural network, and uses standard backpropagation
techniques to learn the weights. The structure revision component uses
qualitative analysis to suggest revisions to the network when it fails
to predict the data accurately. The first component has been
implemented and we will present results from experiments on real world
classification problems which show our technique to be effective.  We
will also discuss our proposed structure revision algorithm, our plans
for experiments to evaluate the system, as well as some extensions to
the system.
</blockquote>

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<a name="assert-aaai-96.ps.Z"</a>

<b><li>A Novel Application of Theory Refinement to Student Modeling<br></b>
Paul Baffes and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 403-408, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
Theory refinement systems developed in machine learning automatically
modify a knowledge base to render it consistent with a set of
classified training examples. We illustrate a novel application of
these techniques to the problem of constructing a student model for an
intelligent tutoring system (ITS). Our approach is implemented in an
ITS authoring system called Assert which uses theory refinement to
introduce errors into an initially correct knowledge base so that it
models incorrect student behavior. The efficacy of the approach has
been demonstrated by evaluating a tutor developed with Assert with 75
students tested on a classification task covering concepts from an
introductory course on the C++ programming language. The system
produced reasonably accurate models and students who received feedback
based on these models performed significantly better on a post test
than students who received simple reteaching.
</blockquote>

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<a name="assert-jaied-95.ps.Z"</a>

<b><li> Refinement-Based Student Modeling and Automated Bug Library 
Construction<br></b>
Paul Baffes and Raymond Mooney<br>

<cite>Journal of Artificial Intelligence in Education</cite>, 7, 1
(1996), pp. 75-116.<p>


<blockquote>
A critical component of model-based intelligent tutoring sytems is a
mechanism for capturing the conceptual state of the student, which
enables the system to tailor its feedback to suit individual strengths
and weaknesses.  To be useful such a modeling technique must be
<em>practical</em>, in the sense that models are easy to construct, and
<em>effective</em>, in the sense that using the model actually impacts student
learning.  This research presents a new student modeling technique
which can automatically capture novel student errors using only
correct domain knowledge, and can automatically compile trends across
multiple student models.  This approach has been implemented as a
computer program, ASSERT, using a machine learning technique called
<em>theory refinement</em>, which is a method for automatically revising a
knowledge base to be consistent with a set of examples.  Using a
knowledge base that correctly defines a domain and examples of a
student's behavior in that domain, ASSERT models student errors by
collecting any refinements to the correct knowledege base which are
necessary to account for the student's behavior.  The efficacy of the
approach has been demonstrated by evaluating ASSERT using 100 students
tested on a classification task covering concepts from an introductory
course on the C++ programming language.  Students who received
feedback based on the models automatically generated by ASSERT
performed significantly better on a post test than students who
received simple teaching.
</blockquote>

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<a name="banner-icnn-96.ps.Z"</a>

<b><li>Revising Bayesian Network Parameters Using Backpropagation<br></b>
Sowmya Ramachandran and Raymond J. Mooney<br>

<cite>Proceedings of the International Conference on Neural
Networks (ICNN-96)</cite>, Special Session on Knowledge-Based Artificial
Neural Networks, Washington DC, June 1996. <p>

<blockquote>
The problem of learning Bayesian networks with hidden variables is known to
be a hard problem. Even the simpler task of learning just the conditional
probabilities on a Bayesian network with hidden variables is hard. In this
paper, we present an approach that learns the conditional probabilities on
a Bayesian network with hidden variables by transforming it into a
multi-layer feedforward neural network (ANN). The conditional probabilities
are mapped onto weights in the ANN, which are then learned using standard
backpropagation techniques. To avoid the problem of exponentially large
ANNs, we focus on Bayesian networks with noisy-or and noisy-and
nodes. Experiments on real world classification problems demonstrate the
effectiveness of our technique.
</blockquote>

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<a name="assert-dissertation-94.tar.Z" </a>

<b> <li> Automatic Student Modeling and Bug Library Construction using Theory 
Refinement <br> </b>  

Paul T. Baffes <br>

Ph.D. Thesis, Department of Computer Sciences, University of Texas at
Austin, December, 1994.<p>

<blockquote>
The history of computers in education can be characterized by a
continuing effort to construct intelligent tutorial programs
which can adapt to the individual needs of a student in a
one-on-one setting. A critical component of these intelligent
tutorials is a mechanism for modeling the conceptual state of the
student so that the system is able to tailor its feedback to suit
individual strengths and weaknesses. The primary contribution of
this research is a new student modeling technique which can
automatically capture novel student errors using only correct
domain knowledge, and can automatically compile trends across
multiple student models into bug libraries. This approach has
been implemented as a computer program, ASSERT, using a machine
learning technique called theory refinement which is a method for
automatically revising a knowledge base to be consistent with a
set of examples. Using a knowledge base that correctly defines a
domain and examples of a student's behavior in that domain,
ASSERT models student errors by collecting any refinements to the
correct knowledge base which are necessary to account for the
student's behavior. The efficacy of the approach has been
demonstrated by evaluating ASSERT using 100 students tested on a
classification task using concepts from an introductory course on
the C++ programming language. Students who received feedback