Knowledge Engineering

1
CS 785, Fall 2001
Knowledge Engineering Classical Methods
George Tecuci tecuci_at_cs.gmu.eduhttp//lalab.gmu.
edu/
Learning Agents LaboratoryDepartment of
Computer Science George Mason University
2
Overview
A scenario for manual knowledge acquisition
Elicitation of experts conception of a domain
Elicitation based on the personal construct theory
Knowledge acquisition for role-limiting methods
Advanced approaches to KB and agent development
3
How are agents built
A knowledge engineer attempts to understand how a
subject matter expert reasons and solves problems
and then encodes the acquired expertise into the
agent's knowledge base. The expert analyzes the
solutions generated by the agent (and often the
knowledge base itself) to identify errors, and
the knowledge engineer corrects the knowledge
base.
4
A Scenario for Manual Knowledge Acquisition
Adapted from
B.G. Buchanan, D. Barstow, R. Bechtal, J.
Bennett, W. Clancey, C. Kulikowski, T. Mitchell,
D.A. Waterman, Constructing an Expert System,
in F. Hayes-Roth, D. Waterman and D. Lenat
(eds), Building Expert Systems, Addison-Wesley,
1983, pp.127-168.
5
Identification of a problem
The director of ORNL faces a problem. EPA
regulations forbid the discharge of quantities of
oil or hazardous chemicals into or upon waters of
the United States, when this discharge violates
specified quality standards. ORNL has
approximately 2000 buildings on a
200-square-mille government reservation, with 93
discharge sites entering White Oak Creek. Oil and
hazardous chemicals are stored and used
extensively at ORNL. The problem is to detect,
monitor, and contain spills of these materials.
6
Investigated solution
Develop a computer system that incorporates the
expertise of people familiar with spill detection
and containment (i.e. a knowledge-based system,
expert system or agent).
A knowledge engineer is assigned the job of
building the system.   The knowledge engineer
becomes familiar with the problem and the
domain.   The knowledge engineer finds an expert
on the subject who agrees to collaborate in
building the system.
7
Scope the problem to solve specify requirements
The knowledge engineer and the expert have a
series of meetings to better identify the
problem and to characterize it informally. They
decide to concentrate on identifying, locating,
and containing the spill

• When an accidental inland spill of an oil or
  chemical occurs,
• an emergency situation may exist, depending on
• the properties and quantity of the substance
  released,
• the location of the substance, and whether or not
  
• the substance enters a body of water.
• The observer of a spill should
• characterize the spill and the probable hazards,
• contain the spill material,
• locate the source of the spill and stop any
  further release,
• notify the Department of Environmental
  Management.

8
Understanding the expertise domain
The knowledge engineer schedules numerous
meetings with the expert to uncover basic
concepts, primitive relations, and definitions
needed to talk about and understand this problem
and its solutions. The following is a sample
dialogue between the knowledge engineer and the
expert
KE Suppose you were told that a spill had been
detected in White Oak Creek one mile before it
enters White Oak Lake. What would you do to
contain the spill? SME That depends on a
number of factors. I would need to find the
source in order to prevent the possibility of
further contamination, probably by checking
drains and manholes for signs of the spill
material. And it helps to know what the spilled
material is. KE How can you tell what it
is? SME Sometimes you can tell what the
substance is by its smell. Sometimes you can
tell by its color, but that's not always reliable
since dyes are used a lot nowadays. Oil,
however, floats on the surface and forms a
silvery film, while acids dissolves completely
in the water. Once you discover the type of
material spilled, you can eliminate any building
that either don't store the material at all or
don't store enough of it to account for the spill.
9
Identify the basic concepts of the domain
The knowledge engineer schedules numerous
meetings with the expert to uncover basic
concepts, primitive relations, and definitions
needed to talk about and understand this problem
and its solutions. The following is a sample
dialogue between the knowledge engineer and the
expert
KE Suppose you were told that a spill had been
detected in White Oak Creek one mile before it
enters White Oak Lake. What would you do to
contain the spill? SME That depends on a
number of factors. I would need to find the
source in order to prevent the possibility of
further contamination, probably by checking
drains and manholes for signs of the spill
material. And it helps to know what the spilled
material is. KE How can you tell what it
is? SME Sometimes you can tell what the
substance is by its smell. Sometimes you can
tell by its color, but that's not always reliable
since dyes are used a lot nowadays. Oil,
however, floats on the surface and forms a
silvery film, while acids dissolves completely
in the water. Once you discover the type of
material spilled, you can eliminate any building
that either don't store the material at all or
don't store enough of it to account for the spill.
10
Identify the basic concepts of the domain (cont.)
As a result of such dialogues, the knowledge
engineer identifies a set of concepts and
features used in this problem
Task Identification of spill material
Attributes of spill Type of spill Oil,
acid Location of spill ltA set of drains and
manholesgt Volume of spill ltA number of
litersgt Attributes of material Color
Silvery, clear, etc. Odor Pungent/choking,
etc. Does it dissolve? Possible locations ltA
set of buildingsgt Amount stored ltA number of
litersgt
11
Choosing the system-building language or tool
During conceptualization, the knowledge engineer
thinks also at a general system-building language
or tool for implementing the knowledge based
system. It was determined that the data are
well-structured and fairly reliable and that the
decision processes involve feedback and parallel
decisions. This suggests the use of a
rule-based language. Therefore the knowledge
engineer decides to use the rule-based language
ROSIE. ROSIE provides a general (rule-based)
inference engine, as well as a formalism for
representing the knowledge in the form of
assertions about objects and inference rules.
ROSIE could be regarded as a very general
expert system shell.
12
Represent the domain concepts object ontology
The knowledge engineer attempts to represent the
concepts in ROSIE's formalism
ASSERT each of BUILDING 3023 and BUILDING 3024 is
a building. ASSERT s6-1 is a source in BUILDING
3023. ASSERT s6-2 is a source in BUILDING
3024. ASSERT s6-1 does hold 2000 gallons of
gasoline. ASSERT s6-2 does hold 50 gallons of
acetic acid. ASSERT each of d6-1 and d6-2 is a
drain. ASSERT each of m6-1 and m6-2 is a
manhole. ASSERT any drain is a location and any
manhole is a location. ASSERT each of diesel oil,
hydraulic oil, transformer oil and gasoline is an
oil. ASSERT each of sulfuric acid, hydrochloric
acid and acetic acid is an acid. ASSERT every oil
is a possible-material of the spill and every
acid is a possible-material of the spill. ASSERT
the spill does smell of some material, e.g.
gasoline, vinegar, diesel oil. ASSERT the spill
does have some odor, e.g., a pungent/choking,
no odor. ASSERT the odor of the spill is, is
not known. ASSERT the spill does form some
appearance, e.g., a silvery film, no
film. ASSERT the spill does, does not dissolve
in water.
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Define the problem solving rules
The knowledge engineer now uses the identified
concepts to represent the expert's method of
determining the spill material as a set of ROSIE
rules
To determine-spill-material 1 IF the spill
does not dissolve in water and the spill does
form a silvery film, THEN let the spill be
oil. 2 IF the spill does dissolve in
water and the spill does form no
film, THEN let the spill be acid. (continued on
next page)
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Define the problem solving rules (cont.)
(continued from previous page) 3 IF the spill
oil and the odor of the spill is
known THEN choose situation IF the spill
does smell of gasoline THEN let the material of
the spill be gasoline with certainty
.9 IF the spill does smell of diesel
oil THEN let the material of the spill be
diesel oil with certainty .8. 4 IF the spill
acid and the odor of the spill is
known, THEN choose situation IF the spill
does have a pungent/choking odor THEN let the
material of the spill be hydrochloric acid
with certainty .7 IF the spill does smell of
vinegar THEN let the material of the spill be
acetic acid with certainty .8. End.
15
Verifying the problem solving rules
The knowledge engineer shows the rules to the
expert and asks for reactions
KE Here are some rules I think capture your
explanation about determining the type of
material spilled and eliminating possible spill
sources. What do you think? SME Uh-huh
(long pause). Yes, that begins to capture it. Of
course if the material is silver
nitrate it will dissolve only partially in the
water. KE I see. Well, let's add that
information to the knowledge base and see
what it looks like.
16
Refinement of the knowledge base
The knowledge engineer may now revise the
knowledge base by reformulating basic domain
concepts, and refining the rules.
Delete ASSERT the spill does, does not
dissolve in water. Add ASSERT the solubility of
the spill is some level - high, moderate,
low. Modify 1 IF the solubility of the
spill is low and the spill does form a silvery
film, THEN let the spill be oil. Add 1.5
IF the solubility of the spill is
moderate, THEN let the material of the spill be
silver-nitrate with certainty .6
17
Main phases of the agent development process
Defining problem to solve and system to be
builtrequirements specification
Understanding the expertise domain
Choosing or building an agent building
tool Inference engine and representation
formalism
Development of the object ontology
Development of problem solving rules or methods
Refinement of the knowledge base
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Elicitation of experts conception of a domain
By eliciting the expert's conception of his/her
expertise domain we mean determining which
concepts apply in the domain, what they mean,
what is their relative place in the domain, what
are the differentiating criteria distinguishing
the similar concepts, and what is the
organizational structure giving these concepts a
coherence for the expert.
19
Elicitation methodology
(based primarily on Gammack, 1987)

• 1. Concept elicitation methods (elicit the
  concepts of the domain i.e. an agreed
  vocabulary)
• 2. Structure elicitation the card-sort
  method (elicit some structure for the concepts)
• Structure representation (formally represent
  that structure in a semantic network)
• Transformation of the representation (transform
  the representation to be used for some desired
  purpose)

20
Concept elicitation methods
Ask the expert to prepare an introductory talk
outlining the whole domain, and to deliver it as
a tutorial session to the knowledge engineer
Tape-record a lecture
Ask the expert to generate a list of typical
concepts and then systematically probe for more
relevant information (e.g. using free
association).
Identify concepts from the index of an expert's
book
21
Concept elicitation methods (cont.)
Unstructured interview of the expert
The questions and the alternative responses are
open-ended.
Example (the interview illustrated before in the
spill application)
KE Suppose you were told that a spill had been
detected in White Oak Creek one mile before it
enters White Oak Lake. What would you do to
contain the spill? SME That depends on a
number of factors. I would need to find the
source in order to prevent the possibility of
further contamination, probably by
Used when the KE wants to explore an
issue. Difficult to plan and conduct.
22
Concept elicitation methods (cont.)
Structured interview of the expert
The questions are fixed in advance.

• Types of structured questions
• Multiple-choice questions (offer specific
  choices, faster tabulation, and less bias due to
  the way the answers are ordered)
• Dichotomous (yes/no) questions
• Ranking scale questions (ask the expert to
  arrange items in a list in order of their
  importance or preference)

Used when the KE wants specific information. It
is goal oriented.
23
Concept elicitation methods (cont.)
Protocol analysis (think-aloud technique)
Systematic collection and analysis of the thought
processes or problem-solving methods of an expert.
Protocols (cases, scenarios) are collected by
asking experts to solve problems and to verbalize
what goes through their minds, stating directly
what they think. The solving process is carried
out in an automatic fashion while the expert
talks. Knowledge engineer does not interrupt or
ask questions. Structuring the information
elicited occurs later when the knowledge engineer
analyzes the protocol.
24
Illustration
Elicitation experiment in the domain of domestic
gas-fired hot water and central heating system
(Gammack, 1987).
Initial interview resulted in about 90 nouns or
compound nouns, both concrete and abstract in
nature. The expert edited this list by removing
synonyms, slips of the tongue, and other aberrant
terms, which reduced the list to 75 familiar
concepts.
25
Illustration
The expert initially considered the dictionary
definition of these concepts to be adequate, but
since there is no guarantee that the expert's own
definition necessarily matches the dictionary
one, a personal definition of the concepts was
given. This produced a few new concepts, such as
"fluid", "safety", and "room". The definitions
indicated that sometimes a concept went beyond
the level of detail given in a general purpose
dictionary and sometimes it meant one very
specific idea in the context of the domain. This
illustrates an important issue Much human
expertise is likely to consist in the personal
and semantic associations (connotative meaning)
that an expert brings to domain concepts and may
result in the invention or appropriation of
personalized terms to describe esoteric or subtle
domain phenomena.
26
Illustration
The domain glossary obtained characterized the
component parts of a central heating system, such
as thermostats and radiators, but also included
general physical terms such as heat and
gravity. A second path through the transcript
yielded 42 relational concepts, usually verbs
(contains, heats, connects to, etc.). These
concepts will be used later to label
relationships between the discovered concepts.
27
Features of the concept elicitation methods
Strengths gives the knowledge engineer an
orientation to the domain. generates much
knowledge cheaply and naturally. not a
significant effort for the expert.
Weaknesses incomplete and arbitrary
coverage the knowledge engineer needs
appropriate training and/or social skills
28
Structure elicitation The Card-Sort Method
The Card-Sort Method (elicit the hierarchical
organization of the concepts) Type the
concepts on small individual index cards. Ask
the expert to group together the related concepts
into as many small groups as possible. Ask the
expert to label each of the groups. Ask the
expert to combine the groups into slightly larger
groups, and to label them. The result will be a
hierarchical organization of the concepts
29
The Card-sort method illustration
Satchwell
Time Switch
Electric Time Controls
Programmer
Thermostat
Thermostat
Set Point
Rotary Control Knob
Gas Control Valve
Electricity
Control
Gas Control
Solenoid
Electrical System
Electrical Supply
Electrical Supply
Electrical Contact
Electrical Components
Fuse
Pump
Mechanical Components
Motorized Valve
Part of the hierarchy of concepts from the
card-sort method
30
Features of the Card-sort method
Strengths gives clusters of concepts and
hierarchical organization splits large domains
into manageable sub-areas easy to do and widely
applicable
Weaknesses incomplete and unguided strict
hierarchy is usually too restrictive
31
Structure representation
Represents the acquired concepts into a semantic
network and acquires additional structural
knowledge Ask the expert to sort the concepts
by considering each concept C as a reference, and
identifying those related to it. Ask the
expert to order the concepts related to C along a
scale from 0 to 100, marked at the side of a
table. The values are read off the scale and
entered in a data matrix. Generate a network
from the matrix, where the nodes are the concepts
and the weighted links represent proximities.
For each pair of concepts identified as
related, ask the expert what that relationship is.
32
Structure representation illustration
33
Developing the representation
For each pair of concepts identified by the
expert as relatable, ask what that relationship
was. This task produced 248 relationships. This
number was effectively reduced to around 124 due
to symmetry. Example of elicited
relationships part-of (radiator, primary
circuit ) feeds (water supply, header
tank) warms (radiator, air) Sometimes relations
were not so direct "boiler supplies heat
that causes water expansion that requires
header tank This suggests the relationship nece
ssitates (boiler, header tank)
34
Features of the structure representation method
Strengths gives information on the domain
structure in the form of a network shows
which links are likely to be meaningful
organizes the elicitation of semantic
relationships
Weaknesses results depend on various parameter
settings requires more time from the expert
combinatorial explosion limits its applicability
35
Transformation of the representation
The elicited knowledge needs to be formally
represented into the representation language of
an expert system shell, such as
Rosie. Additional problem solving knowledge need
to be elicited, depending of the type of system
to be built (e.g. question-answering system,
diagnostic system, repair system, etc.)
36
Elicitation based on the personal construct theory
The personal construct theory
What is a repertory grid
Elicitation of repertory grids
Grid analysis
Features of the repertory grid approach
37
The personal construct theory
A model of human thinking developed in 1955 by
the psychologist George Kelly, to study
psychiatry. Basic idea of the theory Each
person is a scientist with a personal model of
the world around him. He creates personal
constructs that classifies his personal
observations or experience of the world,
developing theories that allow him to anticipate,
and to act in accordance with his
anticipation. A personal construct is therefore
an attribute whose values can distinguish a
subgroup of objects from another one. This
theory was used to develop techniques for
eliciting a subject matter experts personal
constructs with respect to his domain of
expertise.
38
Personal constructs illustration
Example of constructs (or dichotomous
distinctions) characterizing an employee for the
purpose of staff appraisal intelligent -
dim mild - abrasive ideas person - staid
Each person can be rated according to the above
constructs e.g. John is mild (i.e. John is not
abrasive)
The rating can be more refined (along the
mild-abrasive construct)
very mild
mild
very abrasive
abrasive
neutral





mild
abrasive
2
5
4
3
1





mild
abrasive
39
What is a repertory grid
A repertory grid is a representation of a
persons (or experts) view of a particular
problem. It is a two-way classification of a set
of elements based on a set of constructs. Example
of a repertory grid for staff appraisal
40
Elicitation of repertory grids Sample session
with the Pegasus system
Type in your purpose for doing this grid staff
appraisal Name some of the elements Dick, Liz,
Bob, Paul, Ann, Don, Mary Next, one will
elicit constructs from the user using the triad
method.
The triad method (or the minimal context
method) The elements are presented in groups of
three, three being the lowest number that will
produce both a similarity and a difference. The
subject is asked to say in what way two are alike
and thereby different from the third. This is the
emergent pole of the construct. The implicit
pole may be elicited by the difference method (in
what way does the singleton differ from the pair)
or by the opposite method (what would be the
opposite of the description of the pair).
41
Elicitation of repertory grids Sample session
Triad for elicitation of qualities Dick, Liz,
Bob Can you choose two of these elements which
are in some way alike and different from the
other one ? Yes Which is the different one ?
Bob Now I want you to think about what you have
in mind when you separate the pair from the other
one. How can you describe the two ends or poles
of the scale which discriminates Dick and Liz on
the left pole from Bob at the right pole ? left
pole rated 1 intelligent right pole rated
5 dim
42
Elicitation of repertory grids Sample session
According to how you feel about the considered
persons, please assign to each of them a
provisional value from 1 to 5 Dick 1 Liz 1 B
ob 5 Paul 5 Ann 3 Don 3 Mary 5 Ruth 4
Rob 5
At this point, Pegasus has built the following
grid
43
Elicitation of repertory grids Sample session
The session will continue, with Pegasus
presenting other triads for construct
elicitation, and the user defining the
corresponding constructs.
The current grid is
44
Elicitation of repertory grids Sample session
After several constructs have been built, Pegasus
may direct the user in defining new constructs
that distinguish between the elements that are
very similar with respect to current constructs.
45
Elicitation of repertory grids Sample session
Ann and Don are matched at the 90 level. This
means that so far you have not distinguished
between Ann and Don. Do you want to split this ?
Yes Think of a construct which separates Ann
from Don, with Ann on the left pole and Don on
the right pole. left pole rated 1 self
starters right pole rated 5 need a
push According to how you feel about the
considered persons, please assign to each of them
a provisional value from 1 to 5 Dick 2 Liz 1
Bob 5 Paul 5 Ann 1 Don 5 Mary 5
46
Elicitation of repertory grids Sample session
Pegasus may direct the user in defining new
elements that distinguish between the constructs
that are very similar with respect to current
elements.
47
Elicitation of repertory grids Sample session
The two constructs you called intelligent -
dim little supervision-reqd - need
supervision are matched at 66 percent level. This
means that most of the time you are saying
intelligent you are also saying little
supervision required and most of the time you are
saying dim you are also saying need
supervision. Think of another element which is
either intelligent and needs supervision or
little supervision required and dim. Do you know
such a person ? John Type in the ratings for
this element on each construct. Left pole rated
1, right pole rated 5. intelligent -
dim 5 willing - unwilling 2 new boy - old
sweats 3 little supervision reqd - need
supervision 3 motivated - less motivated 2 ...
48
Elicitation of repertory grids Sample session
The final grid is
49
Grid analysis inferring new knowledge from grids
A repertory grid can be viewed a set of feature
vectors, each characterizing an element along the
dimensions indicated by the constructs.
50
Hierarchical clustering of repertory grids
Clusters similar elements and attributes,
prompting the expert to name the clusters
51
Rule induction from repertory grids
The description of each element in the grid is a
positive or a negative example of an output
attribute (e.g. overall rating high)
52
Rule induction from repertory grids (cont.)
The description of each element in the grid is a
positive or a negative example of an output
attribute (e.g. overall rating high) overall-rat
ing-high(Dick) Ü intelligent(Dick),
willing(Dick), new-boy(Dick),
little-sprv-req(Dick), motivated(Dick),
ideas-man(Dick), self-starters(Dick),
... overall-rating-high(Paul) Ü dim(Paul),
unwilling(Paul), experienced(Paul),
need-supervision(Paul), less-motivated(P
aul), not-so-reliable(Paul), mild(Paul),
... A rule for the output attribute is learned
through empirical induction from such
examples overall-rating-high(x) Ü
intelligent(x), little-sprv-reqd(x),
reliable(x), self-starters(x),
creative(x), professional(x)
53
KSS0 An integrated knowledge elicitation and
inductive learning system (Gaines and Shaw, 1992)
Consists of the following modules ELICIT
elicits repertory grids from the expert FOCUS
hierarchically clusters elements and constructs
prompting the expert to add higher-level
constructs structuring the domain PRINCOM
spatially clusters elements and constructs
prompting the expert to add higher-level
constructs structuring the domain SOCIO compares
the structures for the same domain generated by
different experts INDUCT induces rules from the
repertory grid EXPORT transfers the results of
grid elicitation and analysis to an expert system
shell.
54
Features of the repertory grid approach
repertory grids can be easily elicited from a
subject matter expert other concepts and
inference rules can be learned from repertory
grids, although the number of examples is small
more complex knowledge structures are difficult
to generate from repertory grids since the grids
are oriented toward representing declarative
attribute-based knowledge
55
Knowledge acquisition for role-limiting methods
Role-limiting problem solving methods
Propose-and-revise role-limiting problem solving
method
SALT an elicitation tool for propose-and-revise
systems
Knowledge elicitation, refinement, and compilation
Features of the role-limiting approach
56
Role-limiting problem solving methods
Problem solving is the identification, selection,
and implementation of a sequence of actions that
accomplish a task within a specific domain.
A problem solving method provides a means of
identifying, at each step, candidate actions. It
provides one or more mechanisms for selecting
among candidate actions and ensures that the
selected action is implemented.
A role-limiting problem solving method predefines
the task-related control knowledge used. It
typically consists of a simple loop over a
sequence of five to 10 steps. Within a step there
is no control, that is, it makes no difference in
what order the actions are performed. The method
also defines the roles the task-specific
knowledge must play and the forms in which that
knowledge should be represented, and therefore
facilitates the acquisition of this knowledge
from the expert. The price paid for these
assumptions is a more limited applicability.
57
The propose-and-revise role-limiting PS method
For design applications
Input a list of parameters representing customer
requirements Output a list of quantities,
ordering codes and other parameters for all
equipment required, and an equipment
layout. Method (creates a design by proposing
values for design parameters, checking
constraints on those parameters, and revising
values if constraints on proposed parameters are
violated) 1. Extend the design and identify
constraints on the extension just formed. 2.
Identify constraint violations if none, go to
step 1. 3. Suggest potential fixes for a
constraint violation. 4. Select the least costly
fix not yet attempted. 5. Tentatively modify the
design and identify constraints on the
modification just formed. 6. Identify constraint
violations due to the revision if any, go to
4. 7. Remove relationships incompatible with the
revision. 8. If the design is incomplete, go to
1.
58
Knowledge roles
There are three roles that knowledge plays in
this method PROPOSE-A-DESIGN-EXTENSION IDENTI
FY-A-CONSTRAINT on a part of the
design PROPOSE-A-FIX for a constraint
violation There are three types of knowledge
pieces (each for one role) PROCEDURE to
determine the value of a design
parameter CONSTRAINT to identify limits on the
value of a design parameter FIX to suggest
revisions in response to a constraint violation
59
SALT an elicitation tool for propose-and-revise
systems

• (Marcus and McDermott, 1989)
•  
• Elicit PROCEDURE, CONSTRAINT, and FIX knowledge
  pieces, through a menu-driven dialog.
• 2. Build a dependency network that expresses the
  dependencies between the design parameters from
  the elicited knowledge pieces.
• 3. Ask the user to supply a procedure for each
  design parameter that has no associated
  procedure, a constraint for each design
  parameter, and a fix for each constraint
  violation.
• 4. Detect the cycles in the dependency network.
  For each cycle ask the user to supply a procedure
  for determining an initial value of a parameter
  in the cycle.
• 5. Compile the declarative knowledge pieces into
  rules for OPS5.

60
Elicitation of the knowledge pieces
In order to elicit knowledge pieces, SALT
displays a schema of prompts for information
associated with each type of knowledge role.
Calculation PROCEDURE for "car-jamb-return"
1 Name car-jamb-return 2 Precondition door-ope
ning center 3 Procedure calculation 4
Formula platform-width - opening-width / 2 5
Justification center-opening doors look best
when centered on the platform.
Information provided by expert
Information requested by SALT
61
Elicitation of the knowledge pieces
Look-up PROCEDURE for "machine-model" 1 Name
machine-model 2 Precondition none 3
Procedure database-lookup 4 Table
name machine 5 Column with needed value
model 6 Parameter test max-load
suspended-load 7 Parameter test done 8
Ordering column height 9 Optimal smallest 1
0 Justification this procedure is taken from
standards manual iiia, p. 139.
62
Elicitation of the knowledge pieces
For any design parameter defined, the user should
also define a constraint on the possible values
of the parameter.
CONSTRAINT for "car-jamb-return" 1 Constrained
value car-jamb-return 2 Constraint
type maximum 3 Constraint name maximum-car-jamb
-return 4 Precondition door-opening side 5
Procedure calculation 6 Formula panel-width
stringer-quantity 7 Justification this
procedure is taken from installation manual
i, p.12b.
63
Elicitation of the knowledge pieces
For any CONSTRAINT that can be violated the user
has to define a FIX procedure that suggests a
potential fix for the violation.
FIX for the violation of "maximum-car-jamb-return"
1 Violated constraint maximum-car-jamb-retur
n 2 Value to change stringer-quantity 3
Change type increase 4 Step type by-step 5
Step size 1 6 Preference rating 4 7
Reason for preference Changes minor equipment
sizing.
64
Criteria for selecting FIX knowledge pieces
1 Causes no problem 2 Increases maintenance
requirements 3 Makes installation
difficult 4 Changes minor equipment sizing 5
Violates minor equipment constraint 6 Changes
minor contract specifications 7 Requires
special part design 8 Changes major equipment
sizing 9 Changes the building dimensions 10
Changes major contract specifications 11
Increases maintenance costs 12 Compromises
system performance
65
Building of the dependency network
66
Detection of cycles
hoist-cable-quantity suspended-load /
hoist-cable-strength hoist-cable-weight
hoist-cable-unit-weight hoist cable-quantity
hoist-cable-length cable-weight
hoist-cable-weight comp-cable-weight suspended-l
oad cable-weight car-weight
67
Cycle elimination
Ask for a PROCEDURE which provides a first
estimate for one of the parameters in the
loop 1 Name hoist-cable-quantity 2
Precondition none 3 Procedure database-lookup
4 Table name hoist-cable 5 Column with
needed value quantity 6 Parameter
test max-load gt car-weight 7 Parameter
test done 8 Ordering column quantity 9
Optimal smallest 10 Justification this
estimate is the smallest hoist cable quantity
that can be used on any job. Change the role
of the original procedure for hoist-cable-quantity
from PROCEDURE to CONSTRAINT hoist-cable-quant
ity minimum-hoist-cable-quantity minimum-hoist-
cable-quantity suspended-load /
hoist-cable-strength Ask for a FIX knowledge
piece corresponding to the violation of this
constraint 1 Violated constraint minimum-hoist
-cable-quantity 2 Value to change hoist-cable-q
uantity 3 Change type increase 4 Step
type same 5 Preference rating 4 6 Reason for
preference changes minor equipment sizing
68
Updated dependency network
69
Compiling the knowledge base
SALT proceduralizes the domain-specific knowledge
base into rules written in OPS5. PROCEDURE for
"car-jamb-return" 1 Name car-jamb-return 2
Precondition door-opening center 3
Procedure calculation 4 Formula platform-width
- opening-width / 2 5 Justification center-ope
ning doors look best when centered on the
platform. IF Values are available for
door-opening, platform-width and opening-width,
and The value of door-opening is center, and
There is no value for car-jamb-return, THEN Cal
culate the result of the formula platform-width
- opening-width / 2 Assign the result of this
calculation as the value of car-jamb-return. Leav
e a trace that door-opening, platform-width and
opening-width contributed to
car-jamb-return. Leave a declarative
representation of the details of the precondition
and calculation and its justification.
70
Compiling the knowledge base
CONSTRAINT for "car-jamb-return" 1 Constrained
value car-jamb-return 2 Constraint
type maximum 3 Constraint name maximum-car-jamb
-return 4 Precondition door-opening side 5
Procedure calculation 6 Formula panel-width
stringer-quantity 7 Justification this
procedure is from installation manual i,
p.12b. IF Values are available for
door-opening, panel-width, and stringer-quantity,
and The value of door-opening is side,
and There is no value for maximum-car-jamb-return
, THEN Calculate the result of the formula
panel-width stringer-quantity. Assign the
result of this calculation as the value of
maximum-car-jamb-return. Identify this value as
a constraint of type maximum on
car-jamb-return. Leave a trace that
door-opening, panel-width and stringer-quantity
contributed to maximum-car-jamb-return. Leave
a declarative representation of the details of
the precondition and calculation and its
justification.
71
Compiling the knowledge base
FIX for the violation of "maximum-car-jamb-return"
1 Violated constraint maximum-car-jamb-return
2 Value to change stringer-quantity 3 Change
type increase 4 Step type by-step 5 Step
size 1 6 Preference rating 4 7 Reason for
preference Changes minor equipment
sizing. IF There has been a violation of
maximum-car-jamb-return, THEN Try an increase of
stringer-quantity by-steps of 1. This costs 4
because it changes minor equipment
sizing. Try a substitution of side for
door-opening. This costs 8 because it changes
major equipment sizing. Try a decrease of
platform-width by-steps of 2in. This costs 10
because it changes major contract
specifications.
72
Compiling the knowledge base
Three rule types are used to explore the success
of a proposed fix or fix combination in a
look-ahead context before extending the proposed
design on the basis of the proposed revision. IF
maximum-car-jamb-return has been violated,
and The problem-solver has decided on which
changes to try THEN FIND car-jamb-return and
FIND maximum-car-jamb-return. IF The active
command is to FIND car-jamb-return, and Any of
door-opening, platform-width or opening-width has
been revised, and The most recently derived
value (mrdv) of door-opening is center,
and There is no revised value for
car-jamb-return THEN Calculate the formula mrdv
of platform-width - mrdv of opening-width /
2. Assign the result of this calculation as the
value of car-jamb-return. Mark this value as
revised. IF The active command is to FIND
maximum-car-jamb-return, and Any of
door-opening, panel-width or stringer-quantity
has been revised, and The mrdv of door-opening
is side, and There is no revised value for
maximum-car-jamb-return THEN Calculate the
result of the formula mrdv of panel-width mrdv
of stringer-quantity. Assign the result of
this calculation as the value of maximum on
car-jamb-return. Mark this value as revised.
73
Features of the role-limiting approach
Strengths the type and form of the necessary
knowledge are well-defined building the KB
reduces to a menu-driven knowledge elicitation
Weaknesses based on a specific problem solving
method that has a limited domain of
applicability defining the knowledge pieces is
not easy
74
Why it is hard to build agents
The knowledge engineer has to become a kind of
subject matter expert in order to properly
understand experts problem solving knowledge.
This takes time and effort. Experts express
their knowledge informally, using natural
language, visual representations and common
sense, often omitting essential details that are
considered obvious. This form of knowledge is
very different from the one in which knowledge
has to be represented in the knowledge base
(which is formal, precise, and complete). This
modeling and representation of knowledge is long,
painful and inefficient.
75
Limiting factors in developing intelligent agents
76
Advanced approaches to KB and agent development
77
Advanced approaches to KB and agent development
78
Advanced approaches to KB and agent development
Remark Currently, software maintenance is four
times more expensive that software
development. With learning agents that are
directly taught by humans, there is no longer a
distinction between building the agent and
maintaining it.
79
Advanced approaches to KB and agent development
80
Learning agent shell
The learning agent shell is a tool for building
agents. It contains a general problem solving
engine, a learning engine and an empty knowledge
base. Building an agent for a specific
application consists in customizing the shell for
that application and in developing the knowledge
base. The learning engine facilitates the
building of the knowledge base by subject matter
experts and knowledge engineers.
Problem Solving
Ontology Rules
Interface
Learning
81
Disciple learning agent shell
The Disciple learning agent shell can use
imported ontological knowledge and can be taught
directly by subject matter experts to become a
knowledge-based assistant.
The expert teaches the agent to perform various
tasks in a way that resembles how the expert
would teach a person.
The agent learns from the expert, building,
verifying and improving its knowledge base
Mixed-initiative reasoning between the expert
that has the knowledge to be formalized and the
agent that knows how to formalize it.
Problem Solving
Ontology Rules
Interface
Learning
82
Main phases of the agent development process
Modeling of the problem solving process
Customization of the learning agent shell
Ontology import
Ontology refinement
Agent teaching
Mixed-initiative problem solving and learning
83
Recommended reading
G. Tecuci, Lecture Notes on Systematic
Elicitation of Expert Knowledge (required
reading) B.G. Buchanan, D. Barstow, R. Bechtal,
J. Bennett, W. Clancey, C. Kulikowski, T.
Mitchell, D.A. Waterman, Constructing an Expert
System, in F. Hayes-Roth, D. Waterman and D.
Lenat (eds), Building Expert Systems,
Addison-Wesley, 1983, pp.127-168. John G.
Gammack, Different Techniques and Different
Aspects on Declarative Knowledge, in Alison L.
Kidd (ed), Knowledge Acquisition for Expert
Systems A Practical Handbook, Plenum Press,
1987. Shaw M.L.G. and Gaines B.R., An
interactive knowledge elicitation technique using
personal construct technology, in Alison L. Kidd
(ed), Knowledge Acquisition for Expert Systems A
Practical Handbook, Plenum Press, 1987. Marcus
S. and McDermott J., SALT A Knowledge
Acquisition Language for Propose-and-Revise
Systems, Artificial Intelligence, 39 (1989),
pp.1-37. Also in Buchanan B., Wilkins D. (Eds.),
Readings in Knowledge Acquisition and Learning
Automating the Construction and the Improvement
of Programs, Morgan Kaufmann, 1993.