http:^^www.cs.utexas.edu^users^ml^abstracts.html

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

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<a name="chill-aaai-94.ps.Z" </a>

<b> <li> Inducing Deterministic Prolog Parsers From Treebanks:
A Machine Learning Approach </b> <br>  

John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the Twelfth National Conference on
AI</cite>, pp. 748-753, Seattle, WA, July 1994. (AAAI-94) <p>
 
<blockquote>
This paper presents a method for constructing deterministic, context-sensitive,
Prolog parsers from corpora of parsed sentences. Our approach uses recent
machine learning methods for inducing Prolog rules from examples (inductive
logic programming).  We discuss several advantages of this method compared to
recent statistical methods and present results on learning complete parsers
from portions of the ATIS corpus.
</blockquote>

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<a name="ilp-ebl-sigart-94.ps.Z" </a>

<b> <li> Integrating ILP and EBL </b> <br>  

Raymond J. Mooney and John M. Zelle <br> 

<cite> SIGART Bulletin</cite>, Volume 5, number 1, Jan. 1994, pp 12-21. <p>

<blockquote>
This paper presents a review of recent work that integrates methods from
Inductive Logic Programming (ILP) and Explanation-Based Learning (EBL).  ILP
and EBL methods have complementary strengths and weaknesses and a number of
recent projects have effectively combined them into systems with better
performance than either of the individual approaches. In particular, integrated
systems have been developed for guiding induction with prior knowledge
(ML-SMART, FOCL, GRENDEL) refining imperfect domain theories
(FORTE, AUDREY, Rx), and learning effective search-control
knowledge (AxA-EBL, DOLPHIN).
</blockquote>

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<a name="neither-informatica-94.ps.Z" </a>

<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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<a name="lab-masters-93.ps.Z" </a>

<b> <li> Inductive Learning for Abductive Diagnosis </b> <br> 

Cynthia Thompson <br> 

M.A. Thesis, Department of Computer Sciences, University of Texas at Austin, 1993. <p>

<blockquote>
A new system for learning by induction, called LAB, is presented.  LAB
(Learning for ABduction) learns abductive rules based on a set of training
examples.  Our goal is to find a small knowledge base which, when used
abductively, diagnoses the training examples correctly, in addition to
generalizing well to unseen examples.  This is in contrast to past systems,
which inductively learn rules which are used deductively.  Abduction is
particularly well suited to diagnosis, in which we are given a set of symptoms
(manifestations) and we want our output to be a set of disorders which explain
why the manifestations are present.  Each training example is associated with
potentially multiple categories, instead of one, which is the case with typical
learning systems.  Building the knowledge base requires a choice between
multiple possibilities, and the number of possibilities grows exponentially
with the number of training examples.  One method of choosing the best
knowledge base is described and implemented.  The final system is
experimentally evaluated, using data from the domain of diagnosing brain damage
due to stroke.  It is compared to other learning systems and a knowledge base
produced by an expert.  The results are promising: the rule base learned is
simpler than the expert knowledge base and rules learned by one of the other
systems, and the accuracy of the learned rule base in predicting which areas
are damaged is better than all the other systems as well as the expert
knowledge base.
</blockquote>

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<a name="assert-proposal-93.ps.Z" </a>

<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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<a name="dolphin-chill-proposal-93.ps.Z" </a>

<b> <li> Learning Search-Control Heuristics for Logic Programs:
Applications to Speedup Learning and Language Acquisition </b> <br>

John M. Zelle <br> 

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

<blockquote>
This paper presents a general framework, learning search-control heuristics for
logic programs, which can be used to improve both the efficiency and accuracy
of knowledge-based systems expressed as definite-clause logic programs.  The
approach combines techniques of explanation-based learning and recent advances
in inductive logic programming to learn clause-selection heuristics that guide
program execution.  Two specific applications of this framework are detailed:
dynamic optimization of Prolog programs (improving efficiency) and natural
language acquisition (improving accuracy).  In the area of program
optimization, a prototype system, DOLPHIN, is able to transform some
intractable specifications into polynomial-time algorithms, and outperforms
competing approaches in several benchmark speedup domains.  A prototype
language acquisition system, CHILL, is also described.  It is capable of
automatically acquiring semantic grammars, which uniformly incorprate syntactic
and semantic constraints to parse sentences into case-role representations.
Initial experiments show that this approach is able to construct accurate
parsers which generalize well to novel sentences and significantly outperform
previous approaches to learning case-role mapping based on connectionist
techniques.  Planned extensions of the general framework and the specific
applications as well as plans for further evaluation are also discussed.
</blockquote>

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<a name="dolphin-ijcai-93.ps.Z" </a>

<b> <li> Combining FOIL and EBG to Speed-Up Logic Programs </b> <br> 

John M. Zelle and Raymond J. Mooney <br> 

<cite> Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence</cite>, pp. 1106-1111, Chambery, France, 1993. (IJCAI-93) <p>

<blockquote>
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs.  When
these control rules are incorporated into the original program,
significant speed-up may be achieved.  The algorithm is shown to be an
improvement over competing EBL approaches in several domains.
Additionally, the algorithm is capable of automatically transforming
some intractable algorithms into ones that run in polynomial time.
</blockquote>

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<a name="neither-ijcai-93.ps.Z" </a>

<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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<a name="chill-aaai-93.ps.Z" </a>

<b> <li> Learning Semantic Grammars With Constructive Inductive Logic Programming </b> <br> 

John M. Zelle  and Raymond J. Mooney <br> 

<cite> Proceedings of the Eleventh National Conference of the American
Association for Artificial Intelligence</cite>, pp. 817-822,
Washington, D.C. July 1993 (AAAI-93). <p>

<blockquote>
Automating the construction of semantic grammars is a difficult and
interesting problem for machine learning.  This paper shows how the
semantic-grammar acquisition problem can be viewed as the learning of
search-control heuristics in a logic program.  Appropriate control
rules are learned using a new first-order induction algorithm that
automatically invents useful syntactic and semantic categories.
Empirical results show that the learned parsers generalize well to
novel sentences and out-perform previous approaches based on
connectionist techniques.
</blockquote>

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<a name="rapture-connsci-94.ps.Z" </a>

<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) <