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

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

based on the models automatically generated by ASSERT performed
significantly better on a post test than students who received
simple reteaching.
</blockquote>

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<b> <li> Comparing Methods For Refining Certainty Factor Rule-Bases </b> <br> 

J. Jeffrey Mahoney and Raymond J. Mooney <br> 

<cite> Proceedings of the Eleventh International Workshop on Machine
Learning</cite>, pp. 173-180, Rutgers, NJ, July 1994. (ML-94) <p>

<blockquote>
This paper compares two methods for refining uncertain knowledge bases using
propositional certainty-factor rules.  The first method, implemented in the
RAPTURE system, employs neural-network training to refine the certainties
of existing rules but uses a symbolic technique to add new rules.  The second
method, based on the one used in the KBANN system, initially adds a
complete set of potential new rules with very low certainty and allows
neural-network training to filter and adjust these rules.  Experimental results
indicate that the former method results in significantly faster training and
produces much simpler refined rule bases with slightly greater accuracy.
</blockquote>

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<b> <li> Modifying Network Architectures For Certainty-Factor Rule-Base Revision
</b> <br>  
J. Jeffrey Mahoney and Raymond J. Mooney <br> 

<cite> Proceedings of the International Symposium on Integrating
Knowledge and Neural Heuristics 1994</cite>, pp. 75-85, Pensacola, FL,
May 1994. (ISIKNH-94) <p>

<blockquote> 
This paper describes RAPTURE --- a system for revising
probabilistic rule bases that converts symbolic rules into a
connectionist network, which is then trained via connectionist
techniques.  It uses a modified version of backpropagation to refine
the certainty factors of the rule base, and uses ID3's
information-gain heuristic (Quinlan) to add new rules.  Work is
currently under way for finding improved techniques for modifying
network architectures that include adding hidden units using the
UPSTART algorithm (Frean).  A case is made via comparison with fully
connected connectionist techniques for keeping the rule base as close
to the original as possible, adding new input units only as needed.
</blockquote>

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<b> <li> Extending Theory Refinement to M-of-N Rules </b> <br> 

Paul T. Baffes and Raymond J. Mooney <br> 

<cite> Informatica</cite>, 17 (1993), pp. 387-397. <p>

<blockquote>
In recent years, machine learning research has started addressing a problem
known as <em> theory refinement</em>. The goal of a theory refinement learner is
to modify an incomplete or incorrect rule base, representing a domain theory,
to make it consistent with a set of input training examples. This paper
presents a major revision of the EITHER propositional theory refinement
system. Two issues are discussed. First, we show how run time efficiency can
be greatly improved by changing from a exhaustive scheme for computing
repairs to an iterative greedy method. Second, we show how to extend
EITHER to refine MofN rules. The resulting algorithm, Neither (New 
EITHER), is more than an order of magnitude faster and produces
significantly more accurate results with theories that fit the MofN
format. To demonstrate the advantages of NEITHER, we present experimental
results from two real-world domains.
</blockquote>

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<b> <li> Learning to Model Students: Using Theory Refinement to Detect
Misconceptions </b> <br>

Paul T. Baffes <br> 

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1993. <p>


<blockquote>
A new student modeling system called ASSERT is described which uses domain
independent learning algorithms to model unique student errors and to
automatically construct bug libraries. ASSERT consists of two learning phases.
The first is an application of theory refinement techniques for constructing
student models from a correct theory of the domain being tutored. The second
learning cycle automatically constructs the bug library by extracting common
refinements from multiple student models which are then used to bias future
modeling efforts. Initial experimental data will be presented which suggests
that ASSERT is a more effective modeling system than other induction techniques
previously explored for student modeling, and that the automatic bug library
construction significantly enhances subsequent modeling efforts.
</blockquote>

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<b> <li> Symbolic Revision of Theories With M-of-N Rules </b> <br> 

Paul T. Baffes and Raymond J. Mooney <br> 

<cite> Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence</cite>, pp. 1135-1140, Chambery, France, 1993. (IJCAI-93) <p>

<blockquote>
This paper presents a major revision of the EITHER propositional theory
refinement system. Two issues are discussed. First, we show how run time
efficiency can be greatly improved by changing from a exhaustive scheme for
computing repairs to an iterative greedy method. Second, we show how to extend
EITHER to refine M-of-N rules. The resulting algorithm, NEITHER (New EITHER),
is more than an order of magnitude faster and produces significantly more
accurate results with theories that fit the M-of-N format. To demonstrate the
advantages of NEITHER, we present preliminary experimental results comparing it
to EITHER and various other systems on refining the DNA promoter domain theory.
</blockquote>

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<b> <li> Combining Connectionist and Symbolic Learning to Refine Certainty-Factor 
Rule-Bases </b> <br> 

J. Jeffrey Mahoney and Raymond J. Mooney <br> 

<cite> Connection Science</cite>, 5 (1993), pp. 339-364. (Special issue on
Architectures for Integrating Neural and Symbolic Processing) <p>

<blockquote>
This paper describes Rapture --- a system for revising probabilistic knowledge
bases that combines connectionist and symbolic learning methods. Rapture uses
a modified version of backpropagation to refine the certainty factors of a
Mycin-style rule base and it uses ID3's information gain heuristic to add
new rules.  Results on refining three actual expert knowledge bases demonstrate
that this combined approach generally performs better than previous methods.
</blockquote>

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<b> <li> Refinement of First-Order Horn-Clause Domain Theories </b> <br>

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

<cite> Machine Learning</cite> 19,2 (1995), pp. 95-131. <p>

<blockquote> Knowledge acquisition is a difficult and time-consuming
task, and as error-prone as any human activity.  The task of
automatically improving an existing knowledge base using learning
methods is addressed by a new class of systems performing <i> theory
refinement</i>.  Until recently, such systems were limited to
propositional theories.  This paper presents a system, FORTE
(First-Order Revision of Theories from Examples), for refining
first-order Horn-clause theories.  Moving to a first-order
representation opens many new problem areas, such as logic program
debugging and qualitative modelling, that are beyond the reach of
propositional systems.  FORTE uses a hill-climbing approach to revise
theories.  It identifies possible errors in the theory and calls on a
library of operators to develop possible revisions.  The best revision
is implemented, and the process repeats until no further revisions are
possible.  Operators are drawn from a variety of sources, including
propositional theory refinement, first-order induction, and inverse
resolution.  FORTE has been tested in several domains including
logic programming and qualitative modelling.  </blockquote>

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<b> <li> Combining Symbolic and Neural Learning to Revise Probabilistic Theories </b> <br> 

J. Jeffrey Mahoney & Raymond J. Mooney <br> 

<cite> Proceedings of the 1992 Machine Learning Workshop on Integrated
Learning in Real Domains</cite>, Aberdeen Scotland, July 1992. <p>

<blockquote>
This paper describes RAPTURE --- a system for revising probabilistic
theories that combines symbolic and neural-network learning methods. 
RAPTURE uses a modified version of backpropagation to refine the certainty
factors of a Mycin-style rule-base and it uses ID3's information gain heuristic
to add new rules.  Results on two real-world domains demonstrate that this
combined approach performs as well or better than previous methods.
</blockquote>

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<b> <li> Using Theory Revision to Model Students and Acquire Stereotypical Errors </b> <br> 

Paul T. Baffes and Raymond J. Mooney <br> 

<cite> Proceedings of the Fourteenth Annual Conference of the Cognitive
Science Society</cite>, pp. 617-622, Bloomington, IN, July 1992. <p>

<blockquote>