Recent progress in AQUINAS: a knowledge acquisition workbench

30 Pages·2006·1.61 MB·English

egdelwonK noitisiuqcA 1, (1989) 185-214
Recent progress in AQUINAS: a knowledge
acquisition workbench
Jom~ H. BOOSE, DAVID B. SHEMA DNA JEFFREY M. WAHSDARB
gnieoB Advanced Technology ,retneC P.O. Services, Computer Box Boehlg ,64342
,elttaeS AW ,42189 ASU
desaB( no at presented paper a eht dr3 AAAI Acquisition Knowledge for -egdelwonK
desaB Workshop, Systems Banff, November, )8891
Acquiring knowledge from a human expert is a major problem when building a
knowledge-based system. AQUINAS, an expanded version of the expertise transfer
system (ETS), is a knowledge acquisition workbench that combines ideas from
psychology and knowledge-based systems research to support knowledge acquisition
tasks. AQUINAS interviews experts directly and helps them organize, analyse, test,
and refine their knowledge bases. Expertise from multiple experts or other
knowledge sources can be represented and used separately or combined, giving
consensus and dissenting opinions among groups of experts. Results from user
consultations are derived from information propagated through hierarchies. ETS
and AQUINAS have assisted in building knowledge-based systems for several years
at The Boeing Company.
.1 Introduction
This paper describes recent progress on AQUINAS in the areas of knowledge base
performance measurement, knowledge base maintenance, interacting trait con-
straints, consultation graphics, and eliciting strategic and procedural knowledge.
Experiments show how AQUINAS can automatically improve knowledge bases and
even suggest new problem-solving information. Forms of interactive and automatic
machine learning employed by AQUINAS are also discussed.
2. Knowledge-based systems and rapid prototyping
Knowledge-based systems provide environments where preliminary knowledge
bases from human experts may be rapidly prototyped in a matter of days or weeks.
Experts, end-users, and project managers may easily examine the system's behavior
and suggest changes that can be rapidly implemented. The knowledge, often stored
in English-like rules, is readily available for examination, discussion, and modifica-
tion by all project participants. The rapid prototyping capability of these environ-
ments is one reason why they have been successful. One of the first of these
environments, EMYCIN (Buchanan & Shortliffe, 1985) containing the knowledge
transfer tool TEIRESIAS (Davis & Lenat, 1982), is still one of the best examples of
such a rapid prototyping environment. TEIRESIAS helped knowledge engineers
and experts enter rules and parameters, checked consistency, debugged consult-
ations, and even suggested new rules. The interface and rule language could be used
by the expert with a relatively small amount of training.
581
581020/98/3418-2401 + 0/00.30503 © 9891 Limited Press Academic
681
J.H. BOOSE ET AL.
The major hurdle when building these systems is that of acquiring and modeling
the human's expertise. A knowledge engineer--someone familiar with the
knowledge-based system environment or "shell"---interviews the expert, models the
knowledge, embeds it in the system, and reviews the system's behavior with the
expert. The knowledge engineer changes the system based on the suggestions of the
expert. For complex systems, cycles of revision and review can take from xis to
twenty-four months before the system exhibits expertise when using manual
interviewing methods.
Automated knowledge acquisition tools are being developed to help cut down the
revise-and-review cycle time and increase the reliability and maintainability of
knowledge bases. These tools interview experts directly and help them refine,
structure, and test their knowledge. Expertise from many experts may be rapidly
combined together and used. Because models can be built rapidly and easily revised,
AQUINAS can be effectively used as a general-purpose decision aid as well as a tool
for knowledgeen gineering.
3. AQUINAS system description
This section discusses AQUINAS' knowledge representation and basis in personal
construct theory. Knowledge analysis and refinement tools, and the system's rapid
consultation prototyping capability will also be shown.
AQUINAS, an expanded version of the expertise transfer system (ETS; Boose,
,4891 1985, 1986a), si a knowledge acquisition workbench that combines ideas from
psychology and knowledge-based systems researcht o support knowledge acquisition
tasks. These tasks include eliciting distinctions, decomposing problems, combining
uncertain information, incremental testing, integration of data types, automatic
expansion and refinement of the knowledge base, use of multiple sources of
knowledge, use of constraints during inference, and providing process guidance
(Boose & Bradshaw, 1988). AQUINAS interviews experts and helps them analyse,
test, and refine the knowledge base. Expertise from multiple experts or other
knowledge sources can be represented and used separately or combined. Results
from user consultations are derived from information propagated through hierar-
chies. AQUINAS delivers knowledge internally or by creating knowledge bases for
several different expert system shells. Help is given to the expert by a dialog
manager that embodies knowledge acquisition heuristics.
Using AQUINAS, rapid prototypes of knowledge-based systems can be built in as
little as lhr, even when the expert has little understanding of knowledge-based
systems or has no prior training in the use of the tool. The interviewing methods in
AQUINAS are derived from George Kelly's Personal Construct Theory and related
work (Kelly, 1955; Shaw & Gaines, 1987; Boose, 1988)K.e lly's methods and theory
provide a rich framework for modeling the qualitative and quantitative distinctions
inherent in an expert's problem-solving knowledge.
AQUINAS tools mentioned in this paper are explained more fully elsewhere
(Boose, 1988; Boose & Bradshaw, 1988; Bradshaw & Boose, 1989; Kitto & Boose,
1988a; Shema & Boose, 1988). AQUINAS si written in Interlisp and runs on the
Xerox family of Lisp machines. Subsets of AQUINAS also run in an Interlisp
RECENT PROGRESS IN AQUINAS 187
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FIo. 1. Repertory grid for the heart dysrhythmias portion of the medical aid advisor•
version on the DEC Vax, SUN III and IV, and a micro computer "C/UNIX"-based
portable version that runs on a variety of platforms.
3.1. KNOWLEDGE REPRESENTED IN REPERTORY GRIDS
The expert enters and refines knowledge in the form of repertory grids. In a
repertory grid, problem solutions--elements--are elicited and placed across the grid
as column labels, and traits of these solutions--consmtcts--are fisted alongside the
rows of the grid (Fig. 1). Traits are first elicited by presenting groups of solutions
and asking the expert to discriminate among them. Following this, the expert gives
each solution a rating showing where it falls on the trait scale, and gives the relative
importance of each trait. Below are excerpts from an expert building a repertory
grid for a heart dysrhythmias portion of a medical aid advisor.
First, the expert partitions the medical aid advisor into several subproblems, or
cases. Then, for the HEART.DYSRHYTHMIAS case, the expert enters a list of
possible problems (termed sohaions in AQUINAS; the goal of this part of the
system is to diagnose the problem):
Please enter a list of items for expert DBS that are potential solutions for
HEART.DYSRHY-rHM1AS. Enter them one to a line. When you're done, enter a
RETURN
NEW SOLUTION**NORMAL SINUS RHYTHM
NEW SOLUTION**SINUS ARRHYI'HIVIIA
NEW SOLUTION**SINUS TACHYCARDIA
NEW SOLUTION**SUPRAMENTRICULAR TACHYCARDIA
NEW SOLUTION**SINUS BRADYCARDIA
NEW SOLUTION**ATRIAm FLUTTER
188 J.n. aOOSE er .LA
NEW SOLUTION**ATRIAL FIBRILLATION
NEW SOLUTION**NODAL RHYTHM
NEW SOLUTION**IST DEG HEART BLOCK
NEW SOLUTION**2ND DEG HEART BLOCK-WENCKEBACH
NEW SOLUTION**2ND DEG HEART BLOCK-MOBITZ II
NEW SOLUTION**3RD DEG HEART BLOCK
NEW SOLUTIO N**VENTRICULAR TACHYCARDIA
NEW SOLUTION**VENTRICULAR FIBRILLATION
NEW LUTIO SO N**IDIOVENTRICU LAR RHYTHM
NEW SOLUTION**ASYSTOLE
AQUINAS asks the expert for traits that help distinguish between sets of solutions.
Think of an important new trait that two of NORMAL.SINUS.RHYTHM,
SINUS.ARRHY-I-HMIA, and SINUS.TACHYCARDIA share, but that the other one
does not. What is that trait?
NEW TRAIT (EXTREME)**REGULAR RHYTHM
What is that trait's opposite as it applies in this case?
NEW TRAIT (OPPOSITE)**IRREGULAR RHYTHM
What is the name of a scale or concept that describes
REGULAR.RHYTHM/IRREGULAR.RHYTHM?
NEW TRAIT (CONCEPT)**HEART RHYTHM
Think of an important new characteristic that two of SINUS.ARRHYTHMIA,
SINUS.TACHYCARDIA, and SUPRAVENTRICULAR.TACHYCARDIA shae, but that
the other one does not. What is that characteristic?
NEW TRAIT (EXTREME)**SLOW HEART RATE
What is that characteristic's opposite as it applies in this case?
NEW TRAIT (OPPOSITE)**FAST HEART RATE
What is the name of a scale or concept that describes
S LOW.HEART.RATE/FAST.H EART.RA:rE?
NEW TRAIT (CONCEPT)**HEART RATE
Think of an important new attribute that two of SINUS.TACHYCARDIA,
SUPRAVENTRICULAR.TACHYCARDIA, and SlNUS.BRADYCARDIA share, but that
the other one does not. What is that attribute?
NEW TRAIT (EXTREME)**NORMAL P WAVE
What is that attribute's opposite as it applies in this case?
NEW TRAIT (OPPOSlTE)**ABNORMAL P WAVE
What is the name of a scale or concept that describes
N ORMAL.P.WAVE/AB 0 N RMAL.P.WAVE?
NEW TRAIT (CONCEPT)**P WAVE CHARACTERISTIC
RECENT PROGRESS IN AQUINAS 189
The expert then rates each solution on each trait. By default, AQUINAS scales
each trait with ordinal rating values from 1 to 5. The expert can change the rating
value type for a particular trait to nominal, interval, or ratio, and change rating
value ranges and intervals. AQUINAS can assist with this in certain situations, and
even suggest the kinds of changes that should be made.
Rate these solutions on the concept HEART.RHYTHM using a scale of 1 to 5,
where 1 means more like REGULAR.RHYTHM and 5 means more like
IRREGULAR.RHYTHM. If neither one seems to apply, enter NONE. If both seem to
apply, enter BOTH. If you would like help estimating values, enter
USE.BARCHART. If you wish to change the type of the trait, enter CHANGE.
REGULAR.RHYTHM(I) IRREGULAR.RHYTHM(5)
NORMAL.SINUS.RHYTHM** 1
SINUS.ARRHY-I'HMIA** 1
SINUS.TACHYCARDIA** 1
SU PRAVE NTRICU LAR.TACHYCAR DIA** 1
SINUS.BRADYCARDIA** _1
ATRIALFLUTTER** 2
ATRIAL.FIBRILLATION** 5_
NODAL.RHYTHM** 1
1ST.DEG.HEART.BLOCK** 1
2N D.DEG.HEART.BLOCK-WENCKEBACH** 4_
2N D.DEG.HEART.BLOCK-MOBITZ.II** 1
3RD.DEG.HEART.BLOCK** 1 CF.6 5 CF .4
VENTRICU LAR.TACHYCARDIA** 1
VENTRICU LAR.FIBRILLATION** 5_
IDIOVE NTRICULAR.RHY-rHM** 5_
ASYSTOLE** _1
Note that the expert entered a rating distribution for
3RD.DEGREE.HEART.BLOCK. More complex distributions can also be entered
graphically. The expert then rates the importance of each trait's contribution to
finding a solution.
Please rate the relative importance of HEART.RHYTHM
(REGULAR.RHYTHM/IRREGULAR.RHYTHM) on a scale from 5 to 0, where 5
means more important, and 0 means less important.
HEART.RHYTHM** 5_
Please rate the relative importance of HEART.RArE (SLOW
HEART.RATE/FAST.HEART RATE) on a scale from 5 to 0, where 5 means more
important, and 0 means less important.
HEART.RATE** 5_
Please rate the relative importance of P.WAVE.CHARACTERISTIC
(NORMAL.P.WAVE/ABNORMAL.P.WAVE) on a scale from 5 to 0, where 5 means
more important, and 0 means less important.
P.WAVE.CHARACTERISTIC** 4
091 J.H. BOOSE ET AL.
3.2. REPERTORY GRID ANALYSIS
After an initial grid is constructed, AQUINAS helps the expert refine and expand
the knowledge base by invoking a variety of analysis tools. Similarities between
solutions and traits are analysed to help the expert refine useful distinctions and
eliminate those that are inconsequential or redundant. For example,
NORMAL.SINUS;RHYTHM and 1ST.DEG.HEART.BLOCK are found to be
highly matched, and the expert enters a new trait, PR.INTERVAL, to help further
distinguish between them. After the next row is added, AQUINAS asks the expert
to rate all the solutions on the new trait.
The two solutions NORMAL.SINUS.RHYTHM and lST.DEG.HEART.BLOCK are
matched at the 95% level. Can you think of some new trait that would distinguish
between them?
AQU** YES
What is the name of that trait?
WEN TRAIT (CONCEPT)** LAVRETNI.RP
Inductive implications between trait values can show the expert higher levels of
abstraction suggested by patterns of ratings in a repertory grid (Fig. 2). If the expert
disagrees with an implication, AQUINAS helps guide the expert in making changes
that will weaken inappropriate implications. This is one way in which interactive
learning takes place in AQUINAS.
Rating scale types for a particular trait may be changed from the original default
(ordinal scale with ranges from one to five) to other types (nominal, interval, ratio
scales with user-defined ranges) based on the degree of precision required, cost of
eliciting specific knowledge, and representation convenience. AQUINAS helps the
expert change rating scale types in several ways. Here, AQUINAS notices that one
trait has only extreme rating values (all ratings are either 1 to 5), and suggests that
the rating scale type be changed from ordinal to nominal. The expert agrees, and
AQUINAS automatically maps the numerical values to symbolic nominal values.
CITSIRETCARAHC.EVAW.P has only extreme values. Should its trait type be
degnahc from ORDINAL to NOMINAL?
AQU** SEY
IMPLICATIOI'..I.GRAPH OF HEART.DYSRHYTHMIAS • DBS '. DBS.SOLUTION : DB
~..~P. PRESENT,~ ~i~VES.
NORMAL.P. ~VE~--~.__.,..~ ~.
~ N O R I ~ L SRQ.
L~IRONBA~,.,.~ .P. _..EV.aLt
P. ~AVES. ABSENT~,~.~~ ~
~ A B N O R I ~ A L . PR.I NTERVAL
FtG. 2. Implication analysis performed on initial repertory grid.
RECENT PROGRESS IN AQUINAS 191
Use the default method for mapping old to new values of
P.WAVE.CHARACTE RISTIC?
AQU** YES
Mapping for NORMAL.SINUS.RHYTHM from 1 to: NORMAL.P.WAVE 1.00
DEFAULT MAPPING
Mapping for SINUS.ARRHYTHMIA from 1 to: NORMAL.P.WAVE 1.00 DEFAULT
MAPPING
Nominal, ordinal, and interval trait scales were illustrated in the grid in Fig. 1.
3.3. HIERARCHICAL REPERTORY GRIDS
In an important extension to Kelly's methods, AQUINAS allows experts to arrange
repertory grid knowledge in hierarchies. Hierarchical tools in AQUINAS help the
expert build, edit, and analyse knowledge in hierarchies. These hierarchies allow the
I
) H EART.DY~;RHYTHMIAS
MF'DICAL,AI0 ~ LUNG,DISFUNC:TION
ACUT F..ABDOMF.N
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CITSIRETCARAHC.EVAW.P
P-ORS. CORRESPONDENCE
PR. INTERVAL
P, WAVES,PRESENT
SRO EPAHS.
.RP .LAVRETNI CITSIRETCARAHC
FIG. 3. Portions of four hierarchies for the medical aid advisor. Three cases are shown, as well as expert
DBS's solution and trait hierarchies. So far, DBS is the only one who has entered information about
heart dysrhythmias.
192 .J .lE BOOSE ET AL.
expert to break up complex problems into pieces of convenient size and similar
levels of abstraction. Hierarchies in AQUINAS are organized around solutions,
traits, knowledge sources (i.e. experts, databases, sensors), and cases (Figs 3 and 4).
Nodes in the four hierarchies combine to form repertory grids. In the most simple
case, the children of a node in a solution hierarchy supply the solutions along the
top of a grid; the children of a node in a trait hierarchy supply the traits down the
side of a grid (Fig. 4). Rating values within the grid are judgments about the
solutions with respect to each trait (Fig. 5).
Strategies for helping the expert build and refine hierarchies in AQUINAS
include laddering (eliciting specializations and generalizations through dialogs that
pursue the implications of "how" and "why" questions), cluster analysis (experts are
/ I
HEART'OY$ R HYTHMIA$ I
MEDIC,~L.AID ~ LUNG.OISFUHCTION
ACUTE.ABDOMEN
HEART.DYSRHYTH XPMEIRATS .E
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RSYD.RALUCIRTNEV AIMHTYH ~[ VE NTRICUL~R.FIBRILL&T'O N
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/ st/ NT E RAV P U $ IR UC R,~,CYHCAT.R.~JL D IA
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~
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/ HEART.RHYTHM
/HEART.RATE
f/ P.WAV E E.CHARACT IR S TIC
AJS,SOLUTIOhI, TRAIT ~ P-QRS.CORRESPONDENCE
~\ PR.INTERVAL
~ QRS,SHAPE
VAL.CHARACT E R E R,INT P I'R C I T S
Fzo. 4. Another expert, AJS, has entered knowledge for the same case. Tools in AQUINAS were used
to help AJS structure solutions in a'dass hierarchy. The hierarchies are related from top to bottom: given
the HEART.DYSRHYTHMIAS case, experts DBS and AJS exist (other experts could exist for other
cases). Given expert AJS, we see his solution hierarchy (the top of the hierarchy is the class
AJS.SOLUTION). Given that we are looking at the top-level grid in the hierarchy (the children of
AJS.SOLUTION), the immediate children of AJS.SOLUTION.TRAIT apply. Different traits can apply
to different class levels in the solution hierarchy; different solutions could come from different experts,
and so on.
RECENT PROGRESS IN AQUINAS 193
ATED GRID OF HEART.DYSRHYTHMIAS : AJS : AJS.SOLUTION : AJS,SOLUTION.TRAll
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Fro. 5. AJS's top-level grid shows solution classes and traits• Separate grids exist for each class. During
consultations AQUINAS will search the hierarchies to ask questions and infer ratings.
asked to label levels of abstraction represented by junctions in a cluster tree
generated statistically from ratings in a grid; see Fig. 6), implication pattern analysis
(certain patterns of implication may suggest levels of hierarchical subsumption), and
trait Value examination (certain combinations of trait values may suggest that
hierarchical expansion is appropriate).
3.4. DECISIONS AND PERFORMANCE: CONSULTATIONS IN AQUINAS
Knowledge embedded in hierarchical repertory grids can be applied to specific
problems by running consultations. During a consultation, AQUINAS asks the user
to specify observations, preferences, or constraints associated with particular traits
and solutions. Consultation results are displayed as a list of solutions, rank-ordered
by strength of recommendation.
The model of problem-solving used in AQUINAS is that of multiple knowledge
sources (experts) that work together in a common problem solving context (case) by
selecting the best alternatives for each of a sequential set of decisions (solutions).
Alternatives at each step are selected by combining relevant information about
preferences (relativistic reasoning), constraints (absolute reasoning) and evidence
(probabilistic reasoning). This paradigm follows one suggested by Clancey who
suggested that many problems are solved by abstracting data, heuristically mapping
higher level problem descriptions onto solution models, and then refining these
models until specific solutions are found (Clancey, 1986). A variant of a maximum
entropy approach is applied by AQUINAS's reasoning mechanism (Boose &
Bradshaw, 1988; Bradshaw & Boose, ~989). ETS used a reasoning approach based
on rules and certainty factors (Boose, 1986a).
Knowledge from multiple experts may be rapidly combined using AQUINAS.
Users may receive dissenting as well as consensus opinions from groups of experts,
thus getting a full range of possible solutions. Disagreement between the consensus
and the dissenting opinion can be measured to derive a degree of conflict for a
194 J.H. BOOSE ET AL.
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FIo. .6 A solution cluster sisylana si produced from AJS's initial repertory grid. AJS si asked to
form hierarchies by labeling junctions on the cluster. The dusters SINUS.RHYTHMS,
VENTRICULAR.DYSRHYTHMIA, AV.NODE.DYSFUNCTION, and ATRIAL.DYSRHYTHMIA,
4, are in Figure formed. shown
particular consultation. The system can be used for cost-effective group data
gathering (Boose, 1986b, 1988; Schuler, Russo, Boose & Bradshaw, 1989).
Following are portions of a transcript from an AQUINAS consultation using
expertise from both DBS and AJS. The user specifies trait preferences, and
AQUINAS combines the preferences together producing consensus and dissenting
opinions:
Enter a brief description of this consultation
AQU** 56 YO MALE FOUND DOWN, UNKNOWN DOWNTIME t NO CPR ..@ ARRIVAL
What solutions would you like to consider for consultation MEDICAL-AID-ALLEN
Enter them one to a line. If you wish every solution to be considered, enter ALL.