http:^^www.cs.utexas.edu^users^ml^theory-rev.html

This data set contains WWW-pages collected from computer science departments of various universities

Student modeling has been identified as an important component to the long
term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two
basic approaches have evolved to model student misconceptions. One uses a
static, predefined library of user bugs which contains the misconceptions
modeled by the system. The other uses induction to learn student
misconceptions from scratch. Here, we present a third approach that uses a
machine learning technique called theory revision. Using theory revision
allows the system to automatically construct a bug library for use in modeling
while retaining the flexibility to address novel errors.
</blockquote>

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<b> <li> A Preliminary PAC Analysis of Theory Revision </b> <br> 

Raymond J. Mooney <br> 
March 1992 <br>

<cite> Computational Learning Theory and Natural Learning Systems</cite>, 
Vol.  3, T. Petsche, S. Judd, and S. Hanson, Eds., MIT Press, 1995, pp. 43-53. <p>


<blockquote>
This paper presents a preliminary analysis of the sample complexity of theory
revision within the framework of PAC (Probably Approximately Correct)
learnability theory.  By formalizing the notion that the initial theory is
``close'' to the correct theory we show that the sample complexity of an
optimal propositional Horn-clause theory revision algorithm is $O( ( \ln 1 /
\delta + d \ln (s_0 + d + n) ) / \epsilon)$, where $d$ is the {\em syntactic
distance} between the initial and correct theories, $s_0$ is the size of
initial theory, $n$ is the number of observable features, and $\epsilon$ and
$\delta$ are the standard PAC error and probability bounds. The paper also
discusses the problems raised by the computational complexity of theory
revision.
</blockquote>

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<b> <li> Automated Debugging of Logic Programs via Theory Revision </b> <br> 

Raymond J. Mooney & Bradley L. Richards <br> 

<cite> Proceedings of the Second International Workshop on Inductive
Logic Programming</cite>, Tokyo, Japan, June 1992. <p>

<blockquote>
This paper presents results on using a theory revision system to automatically
debug logic programs. FORTE is a recently developed system for revising
function-free Horn-clause theories.  Given a theory and a set of training
examples, it performs a hill-climbing search in an attempt to minimally modify
the theory to correctly classify all of the examples.  FORTE makes use of
methods from propositional theory revision, Horn-clause induction (FOIL),
and inverse resolution.  The system has has been successfully used to debug
logic programs written by undergraduate students for a programming languages
course.
</blockquote>

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<b> <li> Batch versus Incremental Theory Refinement </b> <br> 

Raymond J. Mooney <br> 

<cite> Proceedings of AAAI Spring Symposium on Knowledge
Assimilation</cite>, Standford, CA, March, 1992. <p>

<blockquote>
Most existing theory refinement systems are not incremental. However, any
theory refinement system whose input and output theories are compatible can be
used to incrementally assimilate data into an evolving theory.  This is done by
continually feeding its revised theory back in as its input theory.  An
incremental batch approach, in which the system assimilates a batch of examples
at each step, seems most appropriate for existing theory revision systems.
Experimental results with the EITHER theory refinement system demonstrate
that this approach frequently increases efficiency without significantly
decreasing the accuracy or the simplicity of the resulting theory.  However, if
the system produces bad initial changes to the theory based on only small
amount of data, these bad revisions can ``snowball'' and result in an overall
decrease in performance.
</blockquote>

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<b> <li> A Multistrategy Approach to Theory Refinement </b> <br> 

Raymond J. Mooney & Dirk Ourston <br> 

<cite> Machine Learning: A Multistrategy Approach</cite>, Vol. IV, R.S. Michalski
& G. Teccuci (eds.), pp.141-164, Morgan Kaufman, San Mateo, CA, 1994. <p>

<blockquote>
This chapter describes a multistrategy system that employs independent modules
for deductive, abductive, and inductive reasoning to revise an arbitrarily
incorrect propositional Horn-clause domain theory to fit a set of preclassified
training instances.  By combining such diverse methods, EITHER is able
to handle a wider range of imperfect theories than other theory revision
systems while guaranteeing that the revised theory will be consistent with the
training data.  EITHER has successfully revised two actual expert
theories, one in molecular biology and one in plant pathology. The results
confirm the hypothesis that using a multistrategy system to learn from both
theory and data gives better results than using either theory or data alone.
</blockquote>

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<b> <li> Theory Refinement Combining Analytical and Empirical Methods </b> <br>

Dirk Ourston and Raymond J. Mooney <br> 

<cite> Artificial Intelligence</cite>, 66 (1994), pp. 311--344. <p>

<blockquote>
This article describes a comprehensive approach to automatic theory revision.
Given an imperfect theory, the approach combines explanation attempts for
incorrectly classified examples in order to identify the failing portions of
the theory. For each theory fault, correlated subsets of the examples are used
to inductively generate a correction. Because the corrections are <em>
focused</em>, they tend to preserve the structure of the original theory.  Because
the system starts with an approximate domain theory, in general fewer training
examples are required to attain a given level of performance (classification
accuracy) compared to a purely empirical system. The approach applies to
classification systems employing a propositional Horn-clause theory. The system
has been tested in a variety of application domains, and results are presented
for problems in the domains of molecular biology and plant disease diagnosis.
</blockquote>

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<b> <li> Improving Shared Rules in Multiple Category Domain Theories </b> <br> 

Dirk Ourston and Raymond J. Mooney <br> 

<cite> Proceedings of the Eighth International Machine Learning
Workshop</cite>, pp. 534-538, Evanston, IL, June 1991. <p>

<blockquote>
This paper presents an approach to improving the classification performance of
a multiple category theory by correcting intermediate rules which are shared
among the categories.  Using this technique, the performance of a theory in one
category can be improved through training in an entirely different category.
Examples of the technique are presented and experimental results are given.
</blockquote>

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<b> <li> Constructive Induction in Theory Refinement </b> <br> 

Raymond J. Mooney and Dirk Ourston <br> 

<cite> Proceedings of the Eighth International Machine Learning
Workshop</cite>, pp. 178-182, Evanston, IL. June 1991. <p>

<blockquote>
This paper presents constructive induction techniques recently added to the
EITHER theory refinement system.  These additions allow EITHER to handle
arbitrary gaps at the ``top,'' ``middle,'' and/or ``bottom'' of an incomplete
domain theory.  <i> Intermediate concept utilization</i> employs existing rules
in the theory to derive higher-level features for use in induction.  <i>
Intermediate concept creation</i> employs inverse resolution to introduce new
intermediate concepts in order to fill gaps in a theory that span multiple
levels.  These revisions allow EITHER to make use of imperfect domain theories
in the ways typical of previous work in both constructive induction and theory
refinement.  As a result, EITHER is able to handle a wider range of theory
imperfections than does any other existing theory refinement system.
</blockquote>

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<b> <li> Theory Refinement with Noisy Data </b> <br> 

Raymond J. Mooney and Dirk Ourston <br> 

Technical Report AI 91-153, Artificial Intelligence Lab, University of
Texas at Austin, March 1991. <p>

<blockquote>
This paper presents a method for revising an approximate domain theory based on
noisy data. The basic idea is to avoid making changes to the theory that
account for only a small amount of data. This method is implemented in the
EITHER propositional Horn-clause theory revision system.  The paper
presents empirical results on artificially corrupted data to show that this
method successfully prevents over-fitting.  In other words, when the data is
noisy, performance on novel test data is considerably better than revising the
theory to completely fit the data. When the data is not noisy, noise processing
causes no significant degradation in performance.  Finally, noise processing
increases efficiency and decreases the complexity of the resulting theory.
</blockquote>

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<address><!WA46><a href="http://www.cs.utexas.edu/users/estlin/">[e-mail]</a></address>