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<HTML>
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<TITLE>Papers on Knowledge Acquisition</TITLE>
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<BR>
<P ALIGN=CENTER><B><FONT SIZE=+2 FACE=ARIAL>Publications</B></FONT><BR>
<HR>
<P><FONT FACE=ARIAL><B>Papers on <A HREF="./publications.html#EXPECT">EXPECT</A></P>
<P>Papers on <A HREF="./papers-planning.html">Planning</A></P>
<P>Papers on <A HREF="./papers-ka.html">Knowledge Acquisition</A></P>
<P>Papers on <A HREF="./papers-ontos.html">Ontologies and
Problem-Solving Methods</A></P>
<P>Papers on <A HREF="./papers-ml.html">Machine Learning</A></P></FONT></B>
<HR>
<P ALIGN=LEFT><B><FONT SIZE=+2 FACE=ARIAL>Papers on Knowledge Acquisition</B></FONT><BR></P>
<P><HR>
Jim Blythe.
"Integrating Expectations from Different Sources to Help End Users
Acquire Procedural Knowledge".
<i>Proceedings of IJCAI 2001</i>
(<A
HREF="./papers/blythe-ijcai01.pdf">
PDF file</A>)
</P>
<P><B>Abstract:</B> Role-limiting approaches using explicit theories of
problem-solving have been successful for acquiring knowledge from domain
experts. However most systems using this approach do not support
acquiring procedural knowledge, only instance and type
information. Approaches using interdependencies among different pieces
of knowledge have been successful for acquiring procedural knowledge,
but these approaches usually do not provide all the support that domain
experts require. We show how the two approaches can be combined in such
a way that each benefits from information provided by the other. We
extend the role-limiting approach with a knowledge acquisition tool that
dynamically generates questions for the user based on the problem
solving method. This allows a more flexible interaction pattern. When
users add knowledge, this tool generates expectations for the procedural
knowledge that is to be added. When these procedures are refined, new
expectations are created from interdependency models that in turn refine
the information used by the system. The implemented KA tool provides
broader support than previously implemented systems. Preliminary
evaluations in a travel planning domain show that users who are not
programmers can, with little training, specify executable procedural
knowledge to customize an intelligent system.
</P>
<P><HR>
Jim Blythe, Jihie Kim, Surya Ramachandran and Yolanda Gil.
"An Integrated Environment for Knowledge Acquisition".
Proceedings of the <I>International Conference on Intelligent User
Interfaces 2001</I> (Best Paper Award).
(<A
HREF="./papers/blythe-kim-rama-gil-iui01.pdf">
PDF file</A>)
</P>
<P><B>Abstract:</B> This paper describes an integrated acquisition
interface that includes several techniques previously developed to
support users in various ways as they add new knowledge to an
intelligent system. As a result of this integration, the individual
techniques can take better advantage of the context in which they are
invoked and provide stronger guidance to users. We describe the current
implementation using examples from a travel planning domain, and
demonstrate how users can add complex knowledge to the system.
</P>
<HR>
<P>
Jihie Kim and Yolanda Gil. "Acquiring Problem-Solving Knowledge from End
Users: Putting Interdependency Models to the Test". <i>Proceedings of
AAAI-2000 (to appear) </i> (<a
href="./papers/kim-gil-aaai-2000.pdf">PDF file
</a>)</P>
<p><b>Abstract</b>: Developing tools that allow non-programmers to enter knowledge has been an
ongoing challenge for AI. In recent years researchers have investigated a
variety of promising approaches to knowledge acquisition (KA), but they have
often been driven by the needs of knowledge engineers rather than by end
users. This paper reports on a series of experiments that we conducted in
order to understand how far a particular KA tool that we are developing is
from meeting the needs of end users, and to collect valuable feedback to
motivate our future research. This KA tool, called EMeD, exploits
Interdependency Models derived from a knowledge base in order to guide users
in specifying problem-solving knowledge. One of the challenges of this work
is to devise a methodology and experimental procedure for conducting user
studies of knowledge acquisition tools. We describe how our experiments
helped us address several questions and hypotheses regarding the acquisition
of problem-solving knowledge from end users and the benefits of
Interdependency Models, and discuss what we learned in terms of improving not
only our KA tools but also about KA research.
<HR>
<P>
Jihie Kim and Yolanda Gil. "User Studies of an Interdependency-Based Interface for
Acquiring Problem-Solving Knowledge". <i>Proceedings of the
Intelligent User Interface Conference (IUI-2000)</i> (<a
href="./papers/kim-gil-iui-2000.pdf">PDF file
</a>)</P>
<p><b>Abstract</b>: This paper describes a series of experiments with a range of users to evaluate
an intelligent interface for acquiring problem-solving knowledge to describe
how to accomplish a task. The tool derives the interdependencies between
different pieces of knowledge in the system and uses them to guide the user in
completing the acquisition task. The paper describes results obtained when
the tool was tested with a wide range of users, including end users. The
studies show that our acquisition interface saves users an average of 32% of
the time it takes to add new knowledge, and highlight some interesting
differences across user groups. The paper also describes what are the areas
that need to be addressed in future research in order to make these tools
usable by end users.
<HR>
<P>
Marcelo Tallis. "A Script-Based Approach to Modifying Knowledge-Based Systems". <i>To appear in the International Journal of Human-Computer Studies
</i> (<a
href="./papers/tallis-ijhcs.pdf">PDF file </a>)
<p><b>Abstract: </b>Modifying knowledge-based systems is a complex
activity. One of its difficulties is that several related portions of
the system might have to be changed in order to maintain the coherence
of the system. However, it is difficult for users to figure out what
has to be changed and how. This paper presents a novel approach for
building knowledge acquisition tools that overcomes some of the
limitations of current approaches. In this approach, knowledge of
prototypical procedures for modifying knowledge-based systems is used
to guide users in changing all related portions of a system. These
procedures, which we call <i>knowledge acquisition scripts</i> (or <i>
KA Scripts</i>), capture how related portions of a knowledge-based system
can be changed coordinately. By using KA scripts, a knowledge
acquisition tool would be able to relate individual changes in
different parts of a system, enabling the analysis of each individual
change from the perspective of the overall modification. The paper
also describes the implementation of ETM: a script-based tool that
builds on the EXPECT framework for building knowledge-based systems,
discusses how we have addressed some important
issues of this approach, and describes a preliminary empirical
evaluation of ETM that shows that KA Scripts allow users to perform
knowledge-based systems modification tasks more efficiently.
<HR>
<P>Marcelo Tallis and Yolanda Gil. "Designing Scripts to Guide Users in
Modifying Knowledge-Based Systems". Proceedings of AAAI-99. (<a
href="./papers/tallis-gil-aaai99.pdf">PDF
file </a>)</P>
<B> Abstract: </B> Knowledge Acquisition (KA) Scripts capture typical
modification
sequences that users follow when they modify knowledge bases. KA tools can use
these Scripts to guide users in making these modifications, ensuring that they
follow all the ramifications of the change until it is completed. This paper
describes our approach to design, develop, and organize a library of KA
Scripts. We report the results of three different analysis to develop this
library, including a detailed study of actual modification scenarios in two
knowledge bases. In addition to identifying a good number of KA Scripts, we
found a set of useful attributes to describe and organize the KA
Scripts. These attributes allow us to analyze the size of the library and
generate new KA Scripts in a systematic way. We have implemented a portion of
this library and conducted two different studies to evaluate it. The result of
this evaluation showed a 15 to 52 percent time savings in modifying knowledge
bases and that the library included relevant and useful KA Scripts to assist
users in realistic settings.
<P><HR>
Jihie Kim and Yolanda Gil. "Deriving Expectations to Guide Knowledge
Base Creation". Proceedings of AAAI-99. (<A
HREF="./papers/kim-gil-aaai99.pdf">PDF file</A>)
<p>
<B> Abstract: </B>
Successful approaches to developing knowledge acquisition tools use
expectations of what the user has to add or may want to add, based on how new
knowledge fits within a knowledge base that already exists. When a
knowledge base is first created or undergoes significant extensions and
changes, these tools cannot provide much support. This paper presents an
approach to creating expectations when a new knowledge base is built, and
describes a knowledge acquisition tool that we implemented using this approach
that supports users in creating problem-solving knowledge. As the knowledge
base grows, the knowledge acquisition tool derives more frequent and more
reliable expectations that result from enforcing constraints in the knowledge
representation system, looking for missing pieces of knowledge in the
knowledge base, and working out incrementally the inter-dependencies among the
different components of the knowledge base. Our preliminary evaluations show
a thirty percent time savings during knowledge acquisition. Moreover, by
providing tools to support the initial phases of knowledge base development,
many mistakes are detected early on and even avoided altogether. We believe
that our approach contributes to improving the quality of the knowledge
acquisition process and of the resulting knowledge-based systems as well.</p>
<P><HR>Surya Ramachandran and Yolanda Gil."Knowledge Acquisition for
Configuration Tasks: The EXPECT Approach". Proceedings of the Workshop on
Configuration, AAAI-99. (<A HREF="./papers/AAAI99.pdf">PDF file </A>)
<P><B>Abstract:</B> Configuration systems often use large and complex knowledge bases that need to be maintained and extended over time. The explicit representation of problem-solving knowledge and factual knowledge can greatly enhance the role of a knowledge acquisition tool by deriving from the current knowledge base, the knowledge gaps that must be resolved. This paper details
EXPECT's approach to knowledge acquisition in the configuration domain using the propose-and-revise strategy as an example. EXPECT supports users in a variety of KA tasks like filling knowledge roles, making modifications to the knowledge base including entering new components, classes and even adapting problem-solving strategies for new tasks.
EXPECT's guidance changes as the knowledge base changes, providing a more flexible approach to knowledge acquisition. The paper also examines the possible use of EXPECT as a KA tool in the complex and real world domain of computer configuration. </P>
<HR>
<A NAME="EVAL-99">
Marcelo Tallis and Jihie Kim and Yolanda Gil.
"User Studies of Knowledge Acquisition Tools:
Methodology and Lessons Learned"
<i>Proceedings of the Tenth Banff Knowledge
Acquisition for Knowledge-Based Systems Workshop (KAW-99)</i>,
Banff, Alberta, Canada, April 1999.
(</a><A HREF="./papers/tallis-kim-gil-kaw99.pdf">PDF
file</A>)
<P>
<B> Abstract: </B>
The area of knowledge acquisition research concerned with the
development of knowledge acquisition (KA) tools is in need of a
methodological approach to evaluation. Efforts such as the Sisyphus
experiments have been useful to illustrate particular approaches, but
have not served in practice as testbeds for comparing and evaluating different
alternative approaches.
This paper describes our experimental methodology to conduct studies
and experiments of users modifying knowledge bases with KA tools. We
also report the lessons learned from several experiments that we have
performed. Our hope is that
it will help others design or improve future user evaluations of KA
tools.
We found that performing these experiments is particularly hard
because of difficulties in controlling factors that are
unrelated to the particular claims being tested. We discuss our
ideas for improving our current methodology and some open issues that remain.
<P>
<HR>
Marcelo Tallis.
"A Script-Based Approach to Modifying Knowledge-Based Systems".
<i>Proceedings of the Ninth Banff Knowledge
Acquisition for Knowledge-Based Systems Workshop (KAW-98)</i>,
Banff, Alberta, Canada, April 1998.
(<A HREF="./papers/tallis-kaw98.pdf">PDF file</A>)
<P>
<B> Abstract: </B>
Modifying knowledge-based systems (KBSs) is a complex activity. One
of its difficulties is that several related portions of the KBS
might have to be changed in order to maintain the coherence of the
system. However, it is difficult for users to figure out what has to
be changed and how. This paper presents a novel approach for building
knowledge acquisition (KA) tools that overcomes some of the
limitations of current approaches. In this approach, knowledge of
prototypical procedures for modifying KBSs is used to guide users in
changing all related portions of a KBS. These procedures, which we
call <i>knowledge acquisition scripts</i> (or <i>KA Scripts</i>),
capture how related portions of a KBS can be changed coordinately. By
using KA scripts, a KA tool would be able to relate individual changes
in different parts of a KBS, enabling the analysis of each individual
change from the perspective of the overall modification. The paper
also describes the implementation of ETM: a script-based tool that
builds on the EXPECT framework for building KBSs (Gil, 1994),
discusses how we have addressed some important issues of this
approach, and describes a preliminary empirical evaluation of ETM that
shows that KA Scripts allow users to perform KBSs modification tasks
more efficiently.
<P><HR>
Yolanda Gil and Marcelo Tallis.
"A Script-Based Approach to Modifying Knowledge Bases".
<i>Proceedings of the Fourteenth National Conference on Artificial
Intelligence (AAAI-97)</i>, Providence, RI, July 27-31, 1997.
(<A HREF="./papers/gil-tallis-aaai97.pdf">PDF file </A>)
<P>
<B> Abstract: </B>
Our goal is to build knowledge acquisition tools that support users in
modifying knowledge-based systems. These modifications may require several
individual changes to various components of the knowledge base, which
need to be carefully coordinated to prevent users from leaving the
knowledge-based system in an unusable state. This paper
describes an approach to building knowledge acquisition tools which capture knowledge about
commonly occurring modification sequences and support users in completing
the modifications they start. These sequences, which we call
<i> KA Scripts</i>,
relate individual changes and the effects that they have on
the knowledge base.
We discuss our experience in designing and compiling a library
of KA Scripts. We also describe the implementation of a
tool that uses them and our preliminary evaluations that demonstrate their usability.
<P><HR>
Yolanda Gil and Eric Melz.
"Explicit Representations of Problem-Solving Strategies
to Support Knowledge Acquisition".
<i>Proceedings of the Thirteen National Conference on Artificial
Intelligence (AAAI-96)</i>, Portland, OR, August 4-8, 1996.
(<A HREF="./papers/gil-melz-aaai96.pdf">PDF file </A>)
<P>
<B> Abstract: </B>
Role-limiting approaches support knowledge acquisition (KA) by centering
knowledge base construction on common types of tasks or domain-independent
problem-solving strategies. Within a particular problem-solving strategy,
domain-dependent knowledge plays specific roles. A KA tool then helps a user
to fill these roles. Although role-limiting approaches are useful for
guiding KA, they are limited because they only support users in filling
knowledge roles that have been built in by the designers of the KA system.
EXPECT takes a different approach to KA by representing problem-solving
knowledge explicitly, and deriving from the current knowledge base the
knowledge gaps that must be resolved by the user during KA. This paper
contrasts role-limiting approaches and EXPECT's approach, using the
propose-and-revise strategy as an example. EXPECT not only supports users in
filling knowledge roles, but also provides support in 1) adapting the
problem-solving strategy, 2) changing the types of information to be
acquired about a knowledge role, 3) adding new knowledge roles, and
4) acquiring additional background information about the domain needed
by the knowledge-based system. EXPECT's guidance changes as the knowledge
base changes, providing a more flexible approach to knowledge acquisition.
This work provides evidence supporting the need for explicit representations
in building knowledge-based systems.
<P>
<P><HR>
William R. Swartout and Yolanda Gil.
"EXPECT: A User-Centered Environment for the Development
and Adaptation of Knowledge-Based Planning Aids".
In <i>Advanced Planning Technology: Technological Achievements
of the ARPA/Rome Laboratory Planning Initiative</i>,
ed. Austin Tate. Menlo Park, Calif.: AAAI Press, 1996.
(<A HREF="./papers/swartout-gil-arpi96.pdf">PDF file</A>)
<P>
<B> Abstract: </B>
EXPECT provides an environment for developing knowledge-based
systems that allows end-users to add new knowledge without needing
to understand the details of system organization and
implementation. The key to EXPECT's approach is that it
understands the structure of the knowledge-based system being
built: how it solves problems and what knowledge it needs to
support problem-solving. EXPECT uses this information to guide
users in maintaining the knowledge-based system. We have used
EXPECT to develop a tool for evaluating transportation plans.
<P>
<P><HR>
Bill Swartout and Yolanda Gil.
"Flexible Knowledge Acquisition Through
Explicit Representation of Knowledge Roles".
<i>1996 AAAI Spring Symposium on Acquisition, Learning, and Demonstration:
Automating Tasks for Users</i>, Stanford, CA, March 1996.
(<A HREF="./papers/swartout-gil-sss96.pdf">PDF file </A>)
<P>
<B> Abstract: </B>
A system that acquires knowledge from a user should be able to
reflect upon the knowledge that it has - at each moment - and
understand what kinds of new knowledge it needs to learn. For the
past two decades, research in the area of knowledge acquisition has
been moving towards systems that have access to richer
representations of knowledge about their task. This paper reviews
some well-known knowledge acquisition tools representative of this
trend. It also describes our recent work in EXPECT, a system with
explicit representations of knowledge about the task and the domain
that supports knowledge acquisition for a wider range of tasks and
applications than its predecessors. We hope our observations will
be useful to researchers in user interfaces and in machine learning
concerned with acquiring information from users.
<P>
<P><HR>
Yolanda Gil and Marcelo Tallis.
"Transaction-Based Knowledge Acquisition:
Complex Modifications Made Easier".
<i>In Proceedings of the Ninth Knowledge Acquisition for
Knowledge-Based Systems Workshop</i>, February 26-March 3, 1995.
Banff, Alberta, Canada.
(<A HREF="./papers/gil-tallis-kaw95.pdf">PDF file</A>)
<P>
<B> Abstract: </B> Our goal is to build knowledge acquisition tools that
support users in making a broad range of changes to a knowledge
base, including both factual and problem-solving knowledge. These
changes may require several individual modifications to various
parts of the knowledge base, that need to be carefully coordinated
to prevent users from introducing errors in the knowledge base.
Thus, it becomes essential that our KA tools understand the
consequences of each kind of change that the user may initiate,
detect any harmful side-effects that can be introduced in the
system, and guide the user in resolving them. To address this
issue, we have developed a transaction-based approach to knowledge
acquisition that can support users in making complex modifications
to a knowledge base. A transaction is a sequence of changes that
together modify some aspect of the behavior of a knowledge-based
system, and that when only partially carried out may leave the
knowledge base in an undesirable state. If a user executes a
transaction partially, the knowledge acquisition tool must provide
guidance to finish it and support the user in achieving the desired
modification. This paper also describes our work in extending
EXPECT's knowledge acquisition tool to support transaction-based
mechanisms. EXPECT tracks the possible problems that arise
as a consequence of each individual change to the knowledge base,
keeps information about the context of each change, and uses this
context to resolve the problems detected and to request the user's
intervention if additional information is needed.
<P>
<P><HR>
Bill Swartout and Yolanda Gil.
"EXPECT: Explicit Representations for Flexible Acquisition".
<i>In Proceedings of the Ninth Knowledge Acquisition for
Knowledge-Based Systems Workshop</i>, February 26-March 3, 1995.
Banff, Alberta, Canada.
(<A HREF="./papers/swartout-gil-kaw95.pdf">PDF file</A>)
<P>
<B> Abstract: </B> To create more powerful knowledge acquisition systems, we
not only need better acquisition tools, but we need to change the
architecture of the knowledge based systems we create so that their
structure will provide better support for acquisition. Current
acquisition tools permit users to modify factual knowledge but they
provide limited support for modifying problem solving knowledge.
In this paper, we argue that this limitation (and others) stem from
the use of incomplete models of problem solving knowledge and
inflexible specification of the interdependencies between problem
solving and factual knowledge. We describe the EXPECT architecture
which addresses these problems by providing an explicit
representation for problem solving knowledge and intent. Using this
more explicit representation, EXPECT can automatically derive the
interdependencies between problem solving and factual knowledge.
By deriving these interdependencies from the structure of the
system itself EXPECT supports more flexible and powerful knowledge
acquisition.
<P>
<P><HR>
Yolanda Gil and Cecile Paris.
"Towards Method-Independent Knowledge Acquisition".
<i>Knowledge Acquisition</i>, Special issue on
Machine Learning and Knowledge Acquisition,
Volume 6 Number 2, June 1994.
(<A HREF="./papers/gil-paris-kaj94.pdf">PDF file</A>)
<P>
<B> Abstract: </B>
Rapid prototyping and tool reusability have pushed knowledge
acquisition research to investigate method-specific knowledge
acquisition tools appropriate for predetermined problem-solving
methods. We believe that method-dependent knowledge acquisition is
not the only
approach. The aim of our research is to develop powerful yet
versatile machine learning mechanisms that can be incorporated into
general-purpose but practical knowledge acquisition tools. This paper
shows through examples the practical advantages of this approach. In
particular, we illustrate how existing knowledge can be used to
facilitate knowledge acquisition through analogy mechanisms within a
domain and across domains.
Our sample knowledge acquisition dialogues with a
domain expert illustrate which parts of the process are addressed by
the human and which parts are automated by the tool, in a synergistic
cooperation for knowledge-base extension and refinement. The paper
also describes briefly the EXPECT problem-solving architecture
that facilitates this approach to knowledge acquisition.
<P>
<P><HR>
Yolanda Gil.
"Knowledge Refinement in a Reflective Architecture".
<i>Proceedings of the Twelfth National Conference of Artificial
Intelligence (AAAI-94)</i>, Seattle, WA, August 1994.
(<A HREF="./papers/gil-aaai94.pdf">PDF file</A>) <P>
<B> Abstract: </B>
A knowledge acquisition tool should provide a user with
maximum guidance in extending and debugging a knowledge base, by preventing
inconsistencies and knowledge gaps that may arise inadvertently. Most
current acquisition tools are not very flexible in that they are built
for a predetermined inference structure or problem-solving mechanism,
and the guidance they provide is specific to that inference structure
and hard-coded by their designer. This paper focuses on EXPECT, a
reflective architecture that supports knowledge acquisition
based on an explicit analysis of the structure of a knowledge-based system,
rather than on a fixed set of acquisition guidelines.
EXPECT's problem solver is tightly integrated with LOOM, a
state-of-the-art knowledge representation system. Domain facts and
goals are represented declaratively, and the problem solver keeps
records of their functionality within the task domain. When the user
corrects the system's knowledge, EXPECT tracks any possible
implications of this change in the overall system and cooperates with
the user to correct any potential problems that may arise. The key to
the flexibility of this knowledge acquisition tool is that it
adapts its guidance as the knowledge bases evolve in response to
changes introduced by the user.
<P>
<HR>
<P ALIGN=LEFT><B><FONT SIZE=+2 FACE=ARIAL>Related Papers</B></FONT></P>
<P>Yolanda Gil and Marc Linster.
"Dimensions to Analyze Applications".
<i>Proceedings of the Ninth Knowledge Acquisition for
Knowledge-Based Systems Workshop</i>, February 26-March 3, 1995.
Banff, Alberta, Canada.
(<A HREF="./gil-linster-kaw95.pdf">PDF file </A>)
<P>
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