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

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

Tara A. Estlin<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1996. <p>

<blockquote> 
Planning systems have become an important tool for automating a wide
variety of tasks. Control knowledge guides a planner to find solutions
quickly and is crucial for efficient planning in most domains.
Machine learning techniques enable a planning system to automatically
acquire domain-specific search-control knowledge for different
applications.  Past approaches to learning control information have
usually employed explanation-based learning (EBL) to generate control
rules.  Unfortunately, EBL alone often produces overly complex rules
that actually decrease rather than improve overall planning
efficiency.  This paper presents a novel learning approach for control
knowledge acquisition that integrates explanation-based learning with
techniques from inductive logic programming. In our learning system
SCOPE, EBL is used to constrain an inductive search for control
heuristics that help a planner choose between competing plan
refinements. SCOPE is one of the few systems to address learning
control information for newer, partial-order planners.  Specifically,
this proposal describes how SCOPE learns domain-specific control rules
for the UCPOP planning algorithm. The resulting system is shown to
produce significant speedup in two different planning domains, and to
be more effective than a pure EBL approach.<p>

Future research will be performed in three main areas.  First, SCOPE's
learning algorithm will be extended to include additional techniques
such as constructive induction and rule utility analysis.  Second,
SCOPE will be more thoroughly tested; several real-world planning
domains have been identified as possible testbeds, and more in-depth
comparisons will be drawn between SCOPE and other competing
approaches.  Third, SCOPE will be implemented in a different planning
system in order to test its portability to other planning algorithms.
This work should demonstrate that machine-learning techniques can be a
powerful tool in the quest for tractable real-world planning.<p>
</blockquote>

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<b><li>Lexical Acquisition: A Novel Machine Learning Problem<br></b>
Cynthia A. Thompson and Raymond J. Mooney<br>

Technical Report, Artificial Intelligence Lab, 
University of Texas at Austin, 1996. <p>

<blockquote>
This paper defines a new machine learning problem to which standard
machine learning algorithms cannot easily be applied.  The problem
occurs in the domain of lexical acquisition. The ambiguous and
synonymous nature of words causes the difficulty of using standard
induction techniques to learn a lexicon.  Additionally, negative
examples are typically unavailable or difficult to construct in this
domain.  One approach to solve the lexical acquisition problem is
presented, along with preliminary experimental results on an
artificial corpus.  Future work includes extending the algorithm and
performing tests on a more realistic corpus.
</blockquote>

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<b><li>Advantages of Decision Lists and Implicit Negative in Inductive Logic
Programming<br></b>
Mary Elaine Califf and Raymond J. Mooney<br>

Technical Report, Artificial Intelligence Lab, 
University of Texas at Austin, 1996. <p>

<blockquote>
This paper demonstrates the capabilities of FOIDL, an inductive logic
programming (ILP) system whose distinguishing characteristics are the
ability to produce first-order decision lists, the use of an output
completeness assumption to provide implicit negative examples, and the
use of intensional background knowledge.  The development of FOIDL was
originally motivated by the problem of learning to generate the past
tense of English verbs; however, this paper demonstrates its superior
performance on two different sets of benchmark ILP problems.  Tests on
the finite element mesh design problem show that FOIDL's decision
lists enable it to produce better results than all other ILP systems
whose results on this problem have been reported.  Tests with a selection
of list-processing problems from Bratko's introductory Prolog text demonstrate that the combination of implicit negatives and intensionality allow
FOIDL to learn correct programs from far fewer examples than FOIL.
</blockquote>

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<b><li>Learning Parse Decisions From Examples With Rich Context<br></b>
Ulf Hermjakob and Raymond J. Mooney<br>

Submitted to the 34th Annual Meeting of the Association for Computational Linguistics (ACL-96).<p>

<blockquote>
We present a knowledge and context-based system for parsing natural language
and evaluate it on sentences from the Wall Street Journal.
Applying machine learning techniques, the system uses parse action examples
acquired under supervision to generate a deterministic shift-reduce parser
in the form of a decision structure. It relies heavily on context, as encoded 
in features which describe the morpholgical, syntactical, semantical and
other aspects of a given parse state.
</blockquote>

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<b><li> Corpus-Based Lexical Acquisition For Semantic Parsing <br></b>
Cynthia Thompson<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1995. <p>

<blockquote>
Building accurate and efficient natural language processing (NLP)
systems is an important and difficult problem.  There has been
increasing interest in automating this process.  The lexicon, or the
mapping from words to meanings, is one component that is typically
difficult to update and that changes from one domain to the next.
Therefore, automating the acquisition of the lexicon is an important
task in automating the acquisition of NLP systems.  This proposal
describes a system, WOLFIE (WOrd Learning From Interpreted Examples),
that learns a lexicon from input consisting of sentences paired with
representations of their meanings.  Preliminary experimental results
show that this system can learn correct and useful mappings.  The
correctness is evaluated by comparing a known lexicon to one learned
from the training input.  The usefulness is evaluated by examining the
effect of using the lexicon learned by WOLFIE to assist a parser
acquisition system, where previously this lexicon had to be
hand-built.  Future work in the form of extensions to the algorithm,
further evaluation, and possible applications is discussed.
</blockquote>

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<b><li> Refinement of Bayesian Networks by Combining Connectionist and
Symbolic Techniques <br></b>
Sowmya Ramanchandran<br>

Ph.D. proposal, Department of Computer Sciences, University of Texas
at Austin, 1995. <p>

<blockquote>
Bayesian networks provide a mathematically sound formalism for
representing and reasoning with uncertain knowledge and are as such
widely used. However, acquiring and capturing knowledge in this
framework is difficult. There is a growing interest in formulating
techniques for learning Bayesian networks inductively. While the
problem of learning a Bayesian network, given complete data, has been
explored in some depth, the problem of learning networks with
unobserved causes is still open. In this proposal, we view this
problem from the perspective of theory revision and present a novel
approach which adapts techniques developed for revising theories in
symbolic and connectionist representations.  Thus, we assume that the
learner is given an initial approximate network (usually obtained from
a expert). Our technique inductively revises the network to fit the
data better.  Our proposed system has two components: one component
revises the parameters of a Bayesian network of known structure, and
the other component revises the structure of the network. The
component for parameter revision maps the given Bayesian network into
a multi-layer feedforward neural network, with the parameters mapped
to weights in the neural network, and uses standard backpropagation
techniques to learn the weights. The structure revision component uses
qualitative analysis to suggest revisions to the network when it fails
to predict the data accurately. The first component has been
implemented and we will present results from experiments on real world
classification problems which show our technique to be effective.  We
will also discuss our proposed structure revision algorithm, our plans
for experiments to evaluate the system, as well as some extensions to
the system.
</blockquote>

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<b><li> Refinement-Based Student Modeling and Automated Bug Library 
Construction<br></b>
Paul Baffes and Raymond Mooney<br>

<cite>Journal of Artificial Intelligence in Education</cite>, 7, 1
(1996), pp. 75-116.<p>


<blockquote>
A critical component of model-based intelligent tutoring sytems is a
mechanism for capturing the conceptual state of the student, which
enables the system to tailor its feedback to suit individual strengths
and weaknesses.  To be useful such a modeling technique must be
<em>practical</em>, in the sense that models are easy to construct, and
<em>effective</em>, in the sense that using the model actually impacts student
learning.  This research presents 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.  This approach has been implemented as a
computer program, ASSERT, using a machine learning technique called
<em>theory refinement</em>, 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 knowledege 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 covering 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 teaching.
</blockquote>

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<b><li>Comparative Results on Using Inductive Logic Programming
for Corpus-based Parser Construction<br></b>
John M. Zelle and Raymond J. Mooney<br>

<cite>Symbolic, Connectionist, and Statistical Approaches to Learning for Natural
Language Processing</cite>, S. Wermter, E. Riloff and G. Scheler, Eds,
Spring Verlag, 1996. <p> 

<blockquote>
This paper presents results from recent experiments with CHILL, a
corpus-based parser acquisition system.  CHILL treats language
acquisition as the learning of search-control rules within a logic
program.  Unlike many current corpus-based approaches that use
statistical learning algorithms, CHILL uses techniques from inductive
logic programming (ILP) to learn relational representations.  CHILL is
a very flexible system and has been used to learn parsers that produce
syntactic parse trees, case-role analyses, and executable database
queries.  The reported experiments compare CHILL's performance to that
of a more naive application of ILP to parser acquisition.  The results
show that ILP techniques, as employed in CHILL, are a viable
alternative to statistical methods and that the control-rule framework
is fundamental to CHILL's success.
</blockquote>

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<b><li>Learning the Past Tense of English Verbs Using Inductive Logic 
Programming<br></b>
Raymond J. Mooney and Mary Elaine Califf<br>

<cite>Symbolic, Connectionist, and Statistical Approaches to Learning for Natural
Language Processing</cite>, S. Wermter, E. Riloff and G. Scheler, Eds,
Spring Verlag, 1996. <p>