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Cynthia A. Thompson<br>

<cite>33rd Annual Meeting of the Association of Computational Linguistics</cite>, pp. 335-337, Boston, MA July 1995 (ACL-95). <p>

<blockquote>
A system, WOLFIE, that acquires a mapping of words to their semantic
representation is presented and a preliminary evaluation is performed.
Tree least general generalizations (TLGGs) of the representations of input 
sentences are performed to assist in determining the representations
of individual words in the sentences.  The best guess for a meaning
of a word is the TLGG which overlaps with the highest percentage of 
sentence representations in which that word appears. 
Some promising experimental results on a non-artificial data
set are presented.
</blockquote>

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<b><li>Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs<br></b>

Raymond J. Mooney and Mary Elaine Califf <br>

<cite>Journal of Artificial Intelligence Research</cite>, 3 (1995) pp. 1-24.


<blockquote>
This paper presents a method for inducing logic programs from examples that
learns a new class of concepts called first-order decision lists, defined
as ordered lists of clauses each ending in a cut.  The method, called FOIDL, 
is based on FOIL but employs intensional background knowledge and avoids 
the need for explicit negative examples.  It is particularly useful for 
problems that involve rules with specific exceptions, such as learning the 
past-tense of English verbs, a task widely studied in the context of the 
symbolic/connectionist debate.  FOIDL is able to learn concise, accurate 
programs for this problem from significantly fewer examples than previous 
methods (both connectionist and symbolic).
</blockquote>

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<b> <li> Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes <br> </b>  

Siddarth Subramanian and Raymond J. Mooney <br>

<cite>Working Notes of the IJCAI-95 Workshop on Engneering Problems for Qualitative Reasoning</cite>, Monreal, Quebec, August 1995.<p>

<blockquote> 

This paper describes an approach to diagnosis of systems described by
qualitative differential equations represented as QSIM models.  An
implemented system QDOCS is described that performs multiple-fault,
fault-model based diagnosis, using constraint satisfaction techniques,
of qualitative behaviors of systems described by such models. We
demonstrate the utility of this system by accurately diagnosing
randomly generated faults using simulated behaviors of a portion of
the Reaction Control System of the space shuttle.

</blockquote>

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<b> <li>Learning Qualitative Models for Systems with Multiple Operating Regions<br></b>

Sowmya Ramachandran, Raymond J. Mooney and Benjamin J. Kuipers <br>

<cite>Proceedings of the Eight International Workshop of Qualitative
Reasoning about Physical Systems</cite>, pp. 212-223, Nara, Japan,
June 1994. (QR-94)

<blockquote>
The problem of learning qualitative models of physical systems from
observations of its behaviour has been addressed by several
researchers in recent years. Most current techniques limit themselves
to learning a single qualitative differential equation to model the
entire system.  However, many systems have several qualitative
differential equations underlying them.  In this paper, we present an
approach to learning the models for such systems.  Our technique
divides the behaviours into segments, each of which can be explained
by a single qualitative differential equation.  The qualitative model
for each segment can be generated using any of the existing techniques
for learning a single model.  We show that results of applying our
technique to several examples and demonstrate that it is effective.
</blockquote>

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<b> <li> Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes <br> </b>  

Siddarth Subramanian and Raymond J. Mooney <br>

<cite> Working Papers of the Fifth International Workshop on
Principles of Diagnosis</cite>, pp. 321-325, New Paltz, NY, 1994. <p>

<blockquote>
This paper describes an approach to diagnosis of systems described by
qualitative differential equations represented as QSIM models.  An
implemented system QDOCS is described that performs multiple-fault,
fault-model based diagnosis, using constraint satisfaction techniques,
of qualitative behaviors of systems described by such models. We
demonstrate the utility of this system by accurately diagnosing
randomly generated faults using simulated behaviors of a portion of
the Reaction Control System of the space shuttle.
</blockquote>

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<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
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> Inductive Learning For Abductive Diagnosis <br> </b>  

Cynthia A. Thompson and Raymond J. Mooney <br>

<cite> Proceedings of the Twelfth National Conference on
AI</cite>, pp. 664-669, Seattle, WA, July 1994. (AAAI-94) <p>

<blockquote> A new inductive learning system, LAB (Learning for
ABduction), is presented which acquires abductive rules from a set of
training examples.  The goal is to find a small knowledge base which,
when used abductively, diagnoses the training examples correctly and
generalizes well to unseen examples.  This contrasts with past systems
that inductively learn rules that are used deductively.  Each training
example is associated with potentially multiple categories
(disorders), instead of one as with typical learning systems.  LAB
uses a simple hill-climbing algorithm to efficiently build a rule base
for a set-covering abductive system.  LAB has been experimentally
evaluated and compared to other learning systems and an expert
knowledge base in the domain of diagnosing brain damage due to stroke.
</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. 75-84, 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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<li> <b> Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming
</b> <br>
John M. Zelle, Raymond J. Mooney and Joshua B. Konvisser <br>

<cite> Proceedings of the Eleventh International Workshop
on Machine Learning</cite>, pp. 343-351, Rutgers, NJ, July 1994. (ML-94) <p>

<blockquote>
This paper describes a new method for inducing logic programs from
examples which attempts to integrate the best aspects of existing ILP
methods into a single coherent framework.  In particular, it combines
a bottom-up method similar to GOLEM with a top-down method similar to
FOIL.  It also includes a method for predicate invention similar to
CHAMP and an elegant solution to the ``noisy oracle'' problem which
allows the system to learn recursive programs without requiring a
complete set of positive examples.  Systematic experimental
comparisons to both GOLEM and FOIL on a range of problems are used to
clearly demonstrate the advantages of the approach.
</blockquote>