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

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

MIME-Version: 1.0
Server: CERN/3.0
Date: Tuesday, 07-Jan-97 15:56:08 GMT
Content-Type: text/html
Content-Length: 92952
Last-Modified: Wednesday, 16-Oct-96 15:32:32 GMT

<title>Machine Learning Papers and Abstracts</title>

<h1>Machine Learning Papers and Abstracts</h1>

To view a paper, click on the open book image. <br> 
<br>


<ol>



<! ===========================================================================>

<a name="chill-ilp-96.ps.Z"</a>

<b><li>Inductive Logic Programming for Natural Language Processing</b>
Raymond J. Mooney<br>

<cite>Proceedings of the 6th International Inductive Logic Programming Workshop</cite>, pp. 205-224, Stockholm, Sweden, August 1996. <p>

<blockquote>
This paper reviews our recent work on applying inductive logic
programming to the construction of natural language processing
systems. We have developed a system, CHILL, that learns a parser from
a training corpus of parsed sentences by inducing heuristics that
control an initial overly-general shift-reduce parser.  CHILL learns
syntactic parsers as well as ones that translate English database
queries directly into executable logical form.  The ATIS corpus of
airline information queries was used to test the acquisition of
syntactic parsers, and CHILL performed competitively with recent
statistical methods.  English queries to a small database on
U.S. geography were used to test the acquisition of a complete natural
language interface, and the parser that CHILL acquired was more
accurate than an existing hand-coded system.  The paper also includes
a discussion of several issues this work has raised regarding the
capabilities and testing of ILP systems as well as a summary of our
current research directions.
</blockquote>

<!WA0><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/chill-ilp-96.ps.Z">
<!WA1><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

<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>

<!WA2><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/rapture-dissertation-96.ps.Z">
<!WA3><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

<a name="scope-msl-96.ps.Z"</a>

<b><li> Integrating EBL and ILP to Acquire Control Rules for Planning<br></b>
Tara A. Estlin and Raymond J. Mooney<br>

<cite>Proceedings of the Third International Workshop on
Multi-Strategy Learning</cite>, pp. 271-279, Harpers Ferry, WV, May
1996. (MSL-96).<p>

<blockquote>
Most approaches to learning control information in planning systems
use explanation-based learning to generate control rules.
Unfortunately, EBL alone often produces overly complex rules that
actually decrease planning efficiency.  This paper presents a novel
learning approach for control knowledge acquisition that integrates
explanation-based learning with techniques from inductive logic
programming. EBL is used to constrain an inductive search for
selection heuristics that help a planner choose between competing plan
refinements. SCOPE is one of the few systems to address learning
control information in the newer partial-order planners.
Specifically, SCOPE learns domain-specific control rules for a version
of the UCPOP planning algorithm. The resulting system is shown to
produce significant speedup in two different planning domains.
</blockquote>

<!WA4><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/scope-msl-96.ps.Z">
<!WA5><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

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

<b><li>Comparative Experiments on Disambiguating Word Senses:
An Illustration of the Role of Bias in Machine Learning
<br></b>
Raymond J. Mooney<br>

<cite>Proceedings of the 1996 Conference on Empirical Methods in
Natural Language Processing</cite>, pp. 82-91, Philadelphia, PA, May
1996.. <p>

<blockquote>
This paper describes an experimental comparison of seven different learning
algorithms on the problem of learning to disambiguate the meaning of a word
from context.  The algorithms tested include statistical, neural-network,
decision-tree, rule-based, and case-based classification techniques.  The
specific problem tested involves disambiguating six senses of the word ``line''
using the words in the current and proceeding sentence as context.  The
statistical and neural-network methods perform the best on this particular
problem and we discuss a potential reason for this observed difference.  We
also discuss the role of bias in machine learning and its importance in
explaining performance differences observed on specific problems.
</blockquote>

<!WA6><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/emnlp-96.ps.Z">
<!WA7><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

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

<b><li>Learning to Parse Database Queries using Inductive Logic Programming<br></b>
John M. Zelle and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 1050-1055, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
This paper presents recent work using the CHILL parser acquisition
system to automate the construction of a natural-language interface
for database queries.  CHILL treats parser acquisition as the learning
of search-control rules within a logic program representing a
shift-reduce parser and uses techniques from Inductive Logic
Programming to learn relational control knowledge.  Starting with a
general framework for constructing a suitable logical form, CHILL is
able to train on a corpus comprising sentences paired with database
queries and induce parsers that map subsequent sentences directly into
executable queries.  Experimental results with a complete
database-query application for U.S. geography show that CHILL is able
to learn parsers that outperform a pre-existing, hand-crafted
counterpart.  These results demonstrate the ability of a corpus-based
system to produce more than purely syntactic representations.  They
also provide direct evidence of the utility of an empirical approach
at the level of a complete natural language application.
</blockquote>

<!WA8><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/chill-aaai-96.ps.Z">
<!WA9><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>



<! ===========================================================================>

<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>

<!WA10><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/assert-aaai-96.ps.Z">
<!WA11><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

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

<b><li> Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using
Behavioral Modes<br></b>
Siddarth Subramanian and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 965-970, Portland, OR, August, 1996. (AAAI-96)<p>

<blockquote>
Most model-based diagnosis systems, such as GDE and Sherlock, have
concerned discrete, static systems such as logic circuits and use
simple constraint propagation to detect inconsistencies. However,
sophisticated systems such as QSIM and QPE have been developed for
qualitative modeling and simulation of continuous dynamic systems.  We
present an integration of these two lines of research as implemented
in a system called QDOCS for multiple-fault diagnosis of continuous
dynamic systems using QSIM models. The main contributions of the
algorithm include a method for propagating dependencies while solving
a general constraint satisfaction problem and a method for verifying
the consistency of a behavior with a model across time. Through
systematic experiments on two realistic engineering systems, we
demonstrate that QDOCS demonstrates the best balance of generality,
accuracy, and efficiency among competing methods.
</blockquote>

<!WA12><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/qdocs-aaai96sub.ps.Z">
<!WA13><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

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

<b><li> Multi-Strategy Learning of Search Control for Partial-Order
Planning<br></b>
Tara A. Estlin and Raymond J. Mooney<br>

<cite>Proceedings of the Thirteenth National Conference on Aritificial Intelligence</cite>,
pp. 843-848, Portland, OR, August, 1996. (AAAI-96)<p>


<blockquote>
Most research in planning and learning has involved linear,
state-based planners. This paper presents SCOPE, a system for learning
search-control rules that improve the performance of a partial-order
planner.  SCOPE integrates explanation-based and inductive learning
techniques to acquire control rules for a partial-order planner.
Learned rules are in the form of selection heuristics that help the
planner choose between competing plan refinements.  Specifically,
SCOPE learns domain-specific control rules for a version of the UCPOP
planning algorithm. The resulting system is shown to produce
significant speedup in two different planning domains.
</blockquote>

<!WA14><a href="file://ftp.cs.utexas.edu/pub/mooney/papers/scope-aaai-96.ps.Z">
<!WA15><img align=top src="http://www.cs.utexas.edu/users/ml/paper.xbm"></a><p>

<! ===========================================================================>

<a name="scope-proposal-96.ps.Z"</a>

<b><li> Integrating Explanation-Based and Inductive Learning Techniques
to Acquire Search-Control for Planning<br></b>