CPE/CSC 481:
Knowledge-Based Systems
Dr. Franz J. Kurfess
Computer Science Department
Cal Poly
© 2002-9 Franz J. Kurfess
Expert System Examples 1
Usage of the Slides
 these
slides are intended for the students of my
CPE/CSC 481 “Knowledge-Based Systems” class at Cal
Poly SLO

if you want to use them outside of my class, please let me know
(fkurfess@calpoly.edu)
I
usually put together a subset for each quarter as a
“Custom Show”

 To
to view these, go to “Slide Show => Custom Shows”, select the
respective quarter, and click on “Show”
print them, I suggest to use the “Handout” option
4, 6, or 9 per page works fine
 Black & White should be fine; there are few diagrams where
color is important

© 2002-9 Franz J. Kurfess
Expert System Examples 2
Course Overview
 Introduction
 Knowledge

with Uncertainty
Probability, Bayesian Decision
Making
 Expert

and Inference
Predicate Logic, Inference
Methods, Resolution
 Reasoning

Representation
Semantic Nets, Frames, Logic
 Reasoning

 CLIPS
System Design

Overview
Concepts, Notation, Usage
 Pattern

Matching
Variables, Functions,
Expressions, Constraints
 Expert
System
Implementation

Salience, Rete Algorithm
 Expert
System Examples
 Conclusions and Outlook
ES Life Cycle
© 2002-9 Franz J. Kurfess
Expert System Examples 3
Overview ES Examples
 Motivation
 Objectives
 Chapter



Introduction
Review of relevant concepts
Overview new topics
Terminology
 R1/XCON



System Configuration
Knowledge Representation
Reasoning
 Human
Resources ES
 OSHA Hazard Awareness
Advisor
 Gensym G2 Real-Time
Expert System
 Important Concepts and
Terms
 Chapter Summary
 MYCIN
© 2002-9 Franz J. Kurfess
Expert System Examples 4
Logistics


Introductions
Course Materials


textbooks (see below)
lecture notes



handouts
Web page





PowerPoint Slides will be available on my Web page
http://www.csc.calpoly.edu/~fkurfess
Term Project
Lab and Homework Assignments
Exams
Grading
© 2002-9 Franz J. Kurfess
Expert System Examples 5
Bridge-In
© 2002-9 Franz J. Kurfess
Expert System Examples 6
Pre-Test
© 2002-9 Franz J. Kurfess
Expert System Examples 7
Motivation
 reasons
to study the concepts and methods in the
chapter
 main
advantages
 potential benefits
 understanding
of the concepts and methods
 relationships to other topics in the same or related
courses
© 2002-9 Franz J. Kurfess
Expert System Examples 8
Objectives
 regurgitate

basic facts and concepts
 understand




elementary methods
more advanced methods
scenarios and applications for those methods
important characteristics

differences between methods, advantages, disadvantages, performance,
typical scenarios
 evaluate

application of methods to scenarios or tasks
 apply

methods to simple problems
© 2002-9 Franz J. Kurfess
Expert System Examples 9
R1/XCON
 one
of the first commercially successful expert systems
 application domain



configuration of minicomputer systems
selection of components
arrangement of components into modules and cases
 approach


data-driven, forward chaining
consists of about 10,000 rules written in OPS5
 results



quality of solutions similar to or better than human experts
roughly ten times faster (2 vs. 25 minutes)
estimated savings $25 million/year
© 2002-9 Franz J. Kurfess
Expert System Examples 11
System Configuration

complexity




components



tens or hundreds of components that can be arranged in a multitude of
ways
in theory, an exponential problem
in practice many solutions ``don't make sense'', but there is still a
substantial number of possibilities
important properties of individual components
stored in a data base
constraints

functional constraints derived from the functions a component
performs


e.g. CPU, memory, I/O controller, disks, tapes
non-functional constraints

such as spatial arrangement, power consumption,
© 2002-9 Franz J. Kurfess
Expert System Examples 12
Knowledge Representation
 configuration
 constructed
space
incrementally by adding more and more
components

the correctness of a solution often can only be assessed after it is
fully configured
 subtasks

are identified
make the overall configuration space more manageable
 component
 retrieved
 control
 rules
from the external data base as needed
knowledge
that govern the sequence of subtasks
 constraint
 rules
knowledge
knowledge
that describe properties of partial configurations
© 2002-9 Franz J. Kurfess
Expert System Examples 13
Example Component


partial description of RK611* disk controller
facts are retrieved from the data base and then stored in
templates
RK611*
Class:
Type:
Supported:
Priority Level:
Transfer Rate:
. . .
© 2002-9 Franz J. Kurfess
UniBus module
disk drive
yes
buffered NPR
212
Expert System Examples 14
Example Rule

rules incorporate expertise from configuration experts, assembly
technicians, hardware designers, customer service, etc.
Distribute-MB-Devices-3
If the most current active context is
distributing Massbus devices
& there is a single port disk drive that has not been
assigned to a Massbus
& there are no unassigned dual port disk drives
& the number of devices that each Massbus
should support is known
& there is a Massbus that has been assigned
at least one disk drive and that should support
additional disk drives
& the type of cable needed to connect the disk drive
to the previous device is known
Then assign the disk drive to the Massbus
© 2002-9 Franz J. Kurfess
Expert System Examples 15
Configuration Task








check order; identify and correct omissions, errors
configure CPU; arrange components in the CPU cabinet
configure UniBus modules; put modules into boxes, and
boxes into expansion cabinets
configure panels; assign panels to cabinets and associate
panels with modules
generate floor plan; group components and devices
determine cabling; select cable types and calculate distances
between components
this set of subtasks and its ordering is based on expert
experience with manual configurations
© 2002-9 Franz J. Kurfess
Expert System Examples 16
Reasoning
 data-driven
(forward chaining)
 components
are specified by the customer/sales person
 identify a configuration that combines the selected
components into a functioning system
 pattern
matching
 activates
 execution
appropriate rules for particular situations
control
a
substantial portion of the rules are used to determine
what to do next
 groups of rules are arranged into subtasks
© 2002-9 Franz J. Kurfess
Expert System Examples 17
Performance Evaluation
 notoriously
difficult for expert systems
 evaluation criteria
 usually
very difficult to define
 sometimes comparison with human experts is used
 empirical
evaluation
 Does
the system perform the task satisfactorily?
 Are the users/customers reasonably happy with it?
 benefits
 faster,
fewer errors, better availability, preservation of
knowledge, distribution of knowledge, etc.
 often based on estimates
© 2002-9 Franz J. Kurfess
Expert System Examples 18
Development of R1/XCON
 R1
prototype
 the
initial prototype was developed by Carnegie Mellon
University for DEC
 XCON
commercial system
 used
for the configuration of various minicomputer system
families
 first VAX 11/780, then VAX 11/750, then other systems
 reimplementation



more systematic approach to the description of control knowledge
clean-up of the knowledge base
performance improvements
© 2002-9 Franz J. Kurfess
Expert System Examples 19
Extension of R1/XCON
 addition


wider class of data
additional computer system families
 new



of new knowledge
components
refined subtasks
more detailed descriptions of subtasks
revised descriptions for performance or systematicity reasons
 extended

configuration of ``clusters''

tightly interconnected multiple CPUs
 related

task definition
system XSEL
tool for sales support
© 2002-9 Franz J. Kurfess
Expert System Examples 20
Summary R1/XCON
 commercial


success
after initial reservations within the company, the system was fully
accepted and integrated into the company's operation
widely cited as one of the first commercial expert systems
 domain-specific

the availability of enough knowledge about what to do next was critical
for the performance and eventual success of the system
 suitability


control knowledge
of rule-based systems
appropriate vehicle for the encoding of expert knowledge
subject to a good selection of application domain and task
© 2002-9 Franz J. Kurfess
Expert System Examples 21
MYCIN
based on a presentation by
Adam Gray, CSC 481 W04
some modifications by Franz J.
Kurfess, W05, W06
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 22
Overview
 History
 DENDRAL
 MYCIN
 Background
 Knowledge
Representation
 Knowledge Manipulation
 Uncertainty
 Performance Evaluation
 Advantages and Problems
 References
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 23
DENDRAL


Commonly considered the first expert system
Developed at Stanford in the late 1960s




Ed Feigenbaum (a CSC Professor)
Bruce Buchanan (a philosopher turned computer scientist)
Joshua Lederberg (a Nobel Laureate Geneticist)
Analyzed NMR mass spectrogram data to determine the
geometric arrangement of atoms in a molecule
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 24
MYCIN Background

Medical expert system


Developed at Stanford in the 1970s by Feigenbaum, Buchanan and
Ted Shortliffe (a doctor)
Recommended therapy for blood/meningitis infections



the diagnosis normally involved growing cultures of the infecting organism
(48 hours)
Doctors had to come up with quick guesses about likely problems
 Prescribe drugs to deal with immediate problems
Developed to explore how doctors make these rough, but important,
guesses with partial information

Also important in practice as there are many junior doctors or nonspecialized doctors
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 25
MYCIN Implementation
 Goal-directed
system that uses a basic backwardchaining technique
 450 Rules written in LISP
 Performed as well as some experts and significantly
better than junior doctors
 Never actually used in practice
 Not
due to its performance
 But rather ethical and legal issues
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 26
Example Rule
 If
 The
site of the culture is blood
 The gram of the organism is neg
 The morphology of the organism is rod
 The burn of the patient is serious
Then
there is weakly suggestive evidence (0.4) that the identity
of the organism is pseudomonas
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 27
Representation

Rules had no variables, contexts instead



MYCIN dealt with a number of implicit variables
For example there could be a patient, a culture, a few
infectious organisms.
MYCIN’s knowledge structured into “objectparameter-value” triples



“culture” would be an object
“site” would be a parameter of “culture”
a possible value of this parameter would be “blood”
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 28
Manipulation
 MYCIN
starts out with a rule that says
 If
there is an organism requiring therapy, then, compute
the possible therapies and pick the best one
 First
 if
tries to see if the disease is known
it isn’t begins reasoning process
 Basic
routine in MYCIN
 attempt
to find the value of a parameter
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 29
Finding Values


Depending on the type of data may ask user if the value is
known
Tried to ask the most general question possible, so as not to
become annoying or repetitive


E.g., if MYCIN wants to know if morphology of organism is rod, will
ask “What is morphology of organism?” rather than a specific question
repeatedly
Format of KR is supposed to make questions reasonable
 If
the value is not know, MYCIN does backward
chaining
 Stores a list of rules that might yield a value for each
parameter
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 30
Uncertainty
 Medical
field must reason in the presence of
unknown, incomplete, vague or uncertain information
 MYCIN



used “certainty factors”
initially hard to defend from a sound theoretical viewpoint
theoretical foundations were established later (Dempster-Shafer)
useful to see where knowledge about uncertainty exists, and the
implications it has for the design of the system
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 31
Certainty Factors


Range from –1 (positive it is not the case) to +1 (positive it is
the case).
MYCIN maintains certainty for

possible values of parameters (ultimately, the certainty that you have
a particular disease)



can maintain multiple possible values, each with its own certainty
validity of a rule
MYCIN has rules for combining the certainty factors
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 32
Performance Evaluation
 Shortliffe
used
 10
sample problems
 8 other therapy recommenders

5 faculty at Stanford Med. School, 1 senior resident, 1 senior
postdoctoral researcher, 1 senior student
8
impartial judges gave 1 point per problem
 Max score was 80
 MYCIN: 65, Faculty: 40-60, Fellow: 60, Resident: 45,
Student: 30
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 33
Controls

Judges’ bias for/against computers


Difficulty of problems


Judges did not know who recommended each therapy
Medical student did badly, so problems not easy
Level of Interest


Hypothesis in MYCIN that “knowledge is power”
Have groups with different levels of knowledge
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 34
Good Points
 MYCIN
was good in that
 It
could calculate dosages very precisely
 Dealt well with interactions between drugs

An area in which humans have trouble
 Possesses

nice explanation facilities
Retrieves and displays relevant rules to offer explanation of its
behavior
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 35
Difficulties
 Narrow
 did
in scope
not scale up well to larger problems
 Practical
concerns
 Doctors
have reservations about advise from computers
 Legal issues
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 36
References





E. H. Shortliffe, F. S. Rhame, S. G. Axline, S. N. Cohen, B. G. Buchanan, R. Davis, A.
C. Scott, R. Chavez-Pardo, & W. J. van Melle. “MYCIN: A computer program providing
antimicrobial therapy recommendations”. Clinical Medicine, (Issue):34, 1975.
E. H. Shortliffe. “MYCIN: A rule-based computer program for advising physicians
regarding antimicrobial therapy selection”. Proceedings of the ACM National Congress
(SIGBIO Session), 739. 1974.
Giarratano, J. and G. Riley, ``Expert Systems – Principles and Programming'' 3rd
Edition, PWS Publishing Company, 1998.
“MYCIN: A Quick Case Study”.
<http://www.cee.hw.ac.uk/~alison/ai3notes/section2_5_5.html>.
Russel, Stuart J. and Peter Norvig. “Artificial Intelligence, A Modern Approach”.
Prentice-Hall, Inc., 2003.
© 2002-9 Franz J. Kurfess
A. Gray, 2004
Expert System Examples 37
Human Resources Expert System
 expert
systems to determine conditions and entitlements for
public employees in New South Wales, Australia
 main user groups



employees
managers
HR staff
 accessible


via Internet
http://www.premiers.nsw.gov.au/WorkAndBusiness/WorkingForGovern
ment/HRExpert.htm
some functionality limited to authorized users
 developed
by Softlaw Corporation http://www.softlaw.com.au
© 2002-9 Franz J. Kurfess
Expert System Examples 38
Objectives
 improve
 quality,
 enable
 e.g.
HR advice and information
consistency, timeliness
value-adding strategic functions
work force planning
 extend
use of technology from transaction-based ES
to advice and information systems
© 2002-9 Franz J. Kurfess
Expert System Examples 39
HR Expert Principles
 enhanced
electronic decision tree
 on-line inquiries from users determine branches
 accessible via official HR web sites
 integrated with source documents
 legislation,
© 2002-9 Franz J. Kurfess
personnel handbook, etc.
Expert System Examples 40
HR Expert: Inquiries
© 2002-9 Franz J. Kurfess
Expert System Examples 41
HR Expert: Service History
© 2002-9 Franz J. Kurfess
Expert System Examples 42
Output
 summary
screens
 reports
 letters
 applications
 audit
and forms
reports
© 2002-9 Franz J. Kurfess
Expert System Examples 43
HR Expert: Summary Report
© 2002-9 Franz J. Kurfess
Expert System Examples 44
HR Expert: Full Report
© 2002-9 Franz J. Kurfess
Expert System Examples 45
Project
 phase


3 agencies, 1,250 staff, conducted in 2002-3
demonstrated potential savings, user satisfaction, qualitative benefits
 phase



1 - pilot project
2
extension to all relevant conditions and entitlements
to be operational by May 2004
cited in the report of the Australian Government - Information
Management office as an example
 technology

legislative rulebase technology, STATUTE Expert, by Softlaw Corp.,
Canberra, Australia, http://www.softlaw.com.au
© 2002-9 Franz J. Kurfess
Expert System Examples 46
Benefits
 employees






immediate and up to date
information about conditions
and entitlements
easy access for inquiries
improved data for decisions
increased equity
on-demand generation of
reports
standardized outputs and audit
reports
 Human





© 2002-9 Franz J. Kurfess
direct access to information
about entitlements
less tedious work


Resources
e.g. looking up information
when employees need it
reduced need for repetitive
work
more consistent decisions
on-demand generation of
reports
standardized reports
Expert System Examples 47
Issues
 some
 not
 only
of the input provided by the users
always accurate, up to date
generic conditions and entitlements
 special
 limited
 not
cases not included
coverage
all laws and regulations included
 requires
computer and Web access
 commitment and buy-in from staff and employees
© 2002-9 Franz J. Kurfess
Expert System Examples 48
Status
 operational

update to include recent changes in laws and regulations under way
 current











and in use
modules
Maternity Leave
Study Time
Extended Leave
Recognition of Previous Service
Leaving the Service
Voluntary Redundancy
Travel Compensation, including Meal and Private Motor Vehicle
Allowances
Higher Duties A
llowance Salary
Packaging Agency
List Inquiry
© 2002-9 Franz J. Kurfess
Expert System Examples 49
RuleBurst Demo

a Flash demo of the RuleBurst enviroment is
available at
http://www.ruleburst.com/uploads/files/RuleBurst.html
 a predecessor of RuleBurst was used to develop the HR
Expert application
© 2002-9 Franz J. Kurfess
Expert System Examples 50
References






HR Expert Case Study at
http://www.agimo.gov.au/resources/ppt/2003/030926sb
NSW governmental web site at
http://www.premiers.nsw.gov.au/WorkAndBusiness/WorkingForGovernme
nt/HRExpert.htm
Australian Government Information Management Office Report at
http://www.agimo.gov.au/publications/2004/05/egovt_challenges/accounta
bility/determinations/conclusion
Softlaw Corporation Web site http://www.softlaw.com.au
Softlaw HR Expert Announcement
http://www.softlaw.com.au/content.cfm?categoryid=12&topicid=49&infopa
geid=152
RuleBurst KB Development Environment http://www.ruleburst.com/
sites visited 03-02-05, 02-28-06
© 2002-9 Franz J. Kurfess
Expert System Examples 51
OSHA Hazard Awareness Advisor
 asks
questions about workplace activities,
equipment, materials
 analyzes the user’s answers
 generates a report with common occupational
hazards, applicable OSHA standards, and contacts
 developed by the U.S. Department of Labor,
Occupational Safety & Health Administration (OSHA)
 version 1.0 released in September 1999
 http://www.osha.gov/dts/osta/oshasoft/hazexp.html
© 2002-9 Franz J. Kurfess
Expert System Examples 52
Objectives
 to
help identify and understand common safety and
health hazards in the work place
 especially
 designed
 may
aimed at small businesses
for beginners
be useful for experts as well
 widely
available through online and downloadable
versions
 downloadable
© 2002-9 Franz J. Kurfess
version only for MS Windows
Expert System Examples 53
Limitations
 may
not identify all hazards
 will not determine compliance with OSHA standards
 not a substitute for safety and health professionals
© 2002-9 Franz J. Kurfess
Expert System Examples 54
OSHA Principles
 expert
system technology
 accessible via official OSHA web sites
 integrated with source documents
 standards,
© 2002-9 Franz J. Kurfess
legislation, etc.
Expert System Examples 55
Hazard Awareness Advisor Inquiries
© 2002-9 Franz J. Kurfess
Expert System Examples 56
Hazard Awareness Advisor Report
 highlights
 details
© 2002-9 Franz J. Kurfess
Expert System Examples 57
Report Highlights
+ Evaluate the exposure to chemicals in your workplace.
+ Your site needs a hazard communication program.
+ Inspect ladders and ensure that workers know how to use them safely.
+ Ladders should be at least 3 feet higher than the level they are going to reach.
+ Check that fire exits are unlocked and numerous enough for quick escape of all workers.
+ Keep passageways clear of obstructions.
+ Evaluate personal protective equipment that your workers purchase for themselves.
+ Protective eye wear may be needed to protect against splashes and sprays.
+ Use laser pointers carefully. They can cause eye damage.
+ Please investigate the need for head protection.
+ Please investigate the need for hard toed shoes.
+ Protective gloves may be needed because of injuries by knives or other hand tools.
+ Portable fire extinguishers must have maintenance service at least once a year and a written
record must be kept to show the maintenance or recharge date.
+ Mark fuse boxes or breaker boxes to identify the circuits or equipment they control.
+ Extension cords should not be used as a substitute for permanent wiring.
+ Take care when using cleaning solvents and liquids when cleaning inside a series of deep
cabinets or similar spaces.
+ If you have both ammonia and bleach cleaners, take care in their storage and use. The mixing
of ammonia and bleach can produce dangerous chlorine gas.
© 2002-9 Franz J. Kurfess
Expert System Examples 58
OSHA Report Detail: Portable Ladders
Your answers indicate that your workers use portable ladders. The use of
any ladder is hazardous. Workers may fall from them, fall with them, be
struck by falling ladders or struck by objects dropped from work being
performed on the ladder.
Injuries also result from poor ladder placement: unstable footing, work angle
too steep or too shallow, or placement in front of doors or passageways.
Many serious falls from ladders are the result of workers standing above
the designed working height of the ladder.
The hazards of ladder use can be reduced by careful selection of ladders of
appropriate height and strength, by routine inspection and maintenance,
and by training of workers in safe ladder use.
In order to safely gain access to an upper level such as a roof or platform,
the portable or extension ladder must extend at least 3 feet above the
point of contact. Any portable ladders should be tied off or held in position
during use.
© 2002-9 Franz J. Kurfess
Expert System Examples 59
Project
 preceded




Asbestos in '95
Confined Spaces in '96
Fire Safety in '97
Lead in Construction in '98
 input







by other SHA advisors
from
National Federation of Independent Business,
National Apartment Association,
Synthetic and Organic Chemical Manufacturers Association,
United Brotherhood of Carpenters and Joiners,
Laborers Safety and Health Fund,
International Brotherhood of Teamsters,
other industry and labor organizations
© 2002-9 Franz J. Kurfess
Expert System Examples 60
Benefits
 owners/managers



easy access for inquiries
about potential hazards
quick analysis of the
workplace
generation of a report with
highlights, details, and
pointers for further information
© 2002-9 Franz J. Kurfess
 employees



identification of potential
hazards
improved working conditions
possible compliance issues

standards and regulations
Expert System Examples 61
Issues
 all
of the input provided by the users
 not
always accurate, up to date
 limited
 may
coverage
not identify all hazards
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Expert System Examples 62
Status
 version

1.0 operational and in use since September 1999
update to include recent changes in laws and regulations ???
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Expert System Examples 63
Other OSHA Expert Advisors
 see









http://www.osha.gov/dts/osta/oshasoft/index.html
Asbestos
Confined Space
Electronic Permit Required Confined Spaces (e-PRCS)
Electronic Health and Safety Plan (e-HASP)
Fire Safety
Hazard Awareness
Lead in Construction
Lead in General Industry
Lockout/Tagout



LOTO Plus
SafeCare
$afety Pays
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Expert System Examples 64
References






OSHA Hazard Awareness Advisor, Version 1.0 September 1999,
http://www.osha.gov/dts/osta/oshasoft/hazexp.html
Stern, Ed (1998): “OSHA Unveils Online Hazard Awareness Advisor”,
Access America Government Services,
http://govinfo.library.unt.edu/accessamerica/docs/expertadvisor.html
Stern, Ed (1999): “The OSHA Hazard Awareness Advisor”, PC AI
Magazine, vol 13, no 2, March/April 1999
Shirley, Robin E. (200): “New OSHA Interactive Software Designed to
Help Small Business Owners”, On Target - News for the Small Business
Owner, http://www.reswritingservices.com/osha.html
Virginia Workers’ Compensation Program (2005): “Hazard Assessments”,
http://www.covwc.com/lcarticles/archives/000084.php
OSHA eTools and Electronic Products for Compliance Assistance
http://www.osha.gov/dts/osta/oshasoft/index.html
sites visited 07-14-05, 02-28-06
© 2002-9 Franz J. Kurfess
Expert System Examples 65
Gensym G2
 real-time
expert system
 developed by Gensym Corporation
http://www.gensym.com
 application areas
 chemical,
oil & gas, process manufacturing, discrete
manufacturing, power utilities, water utilities,
telecommunications, government, transportation,
aerospace
 augmented
© 2002-9 Franz J. Kurfess
by additional modules
Expert System Examples 66
Gensym G2 Platform
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Expert System Examples 67
Gensym G2 Use
http://www.gensym.com/images/pages/g2platform.jpg
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Expert System Examples 68
Gensym Development Cycle
http://www.gensym.com/images/pages/xtreme-programming-lifecycle.jpg
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Expert System Examples 69
Ericsson Network Management
System
 use
of G2 for wireless network management
 challenges in wireless networks
 additional

functionality
instant messaging, chat, Web access, photos, videos, …
 increased
size and complexity of the network
 very rapid growth and change rate
 network
management can be very stressful
 constant

stream of alarms
not all are important
 extreme
pressure to identify and fix problems
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Expert System Examples 70
Ericsson FMX
 largely


concentration on the fault management process
reacts to all identified events very quickly






automated system for wireless network management
much faster than humans
more reliable
but less flexible
filters out unimportant messages
allows network operators to concentrate on critical events
consolidates information for critical events
 manages


over 500,000 events per day
500 systems in 100 countries
50 different equipment types
 benefits


increased quality of service
reduced operating expenses
© 2002-9 Franz J. Kurfess
Expert System Examples 71
FMX Screenshot
Input
Condition
Decision
© 2002-9 Franz J. Kurfess
http://www.gensym.com/?p=success_stories&id=13
Outputs
Expert System Examples 72
Dow Chemicals Closed Loop
Optimizer
 energy
management in a large petrochemical plant
in Seadrift, TX
 highly
interdependent systems
 real-time control
 safety-critical
 very high energy costs
 utilization
of waste heat from gas turbines
 internal
energy usage
 excess energy sold to the grid
 manual
control of power generation was problematic
 trade-off
considerations must be made very fast
© 2002-9 Franz J. Kurfess
Expert System Examples 73
Energy Management with G2
 modeling
of the energy system
 sensors
provide input
 important components are modeled
 output controls actuators, informs operators
 previous models of individual systems were not successful
for the overall energy management
 optimization
 determines
the best operational plan for the current
conditions in real time
© 2002-9 Franz J. Kurfess
Expert System Examples 74
Seadrift Functional Diagram
© 2002-9 Franz J. Kurfess
Expert System Examples 75
Seadrift Screenshot
© 2002-9 Franz J. Kurfess
Expert System Examples 76
Seadrift Outcome
 plant
ran in closed loop mode 98 percent of the time
 saved Dow $1.25 million dollars in energy costs over
one year
 even



 user
larger potential for savings
extension to other components and systems in the plant
more sophisticated modeling
usage for other plans
satisfaction
 operators
were skeptical initially, but accepted and used
the system very quickly
 better view of overall plant operations
© 2002-9 Franz J. Kurfess
Expert System Examples 77
References
 Gensym
Corporation http://www.gensym.com
 Gensym “Success Stories”: Dow Chemicals
http://www.gensym.com/?p=success_stories&id=8
 Dow Chemicals Seadrift Plant
http://www.dow.com/ucc/locations/seadrift/about/inde
x.htm
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Expert System Examples 78
Questions
© 2002-9 Franz J. Kurfess
Expert System Examples 79
Figure Example
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Expert System Examples 80
Post-Test
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Expert System Examples 81
Important Concepts and Terms













agenda
backward chaining
common-sense knowledge
conflict resolution
expert system (ES)
expert system shell
explanation
forward chaining
inference
inference mechanism
If-Then rules
knowledge
knowledge acquisition
© 2002-9 Franz J. Kurfess












knowledge base
knowledge-based system
knowledge representation
Markov algorithm
matching
Post production system
problem domain
production rules
reasoning
RETE algorithm
rule
working memory
Expert System Examples 83
Summary Chapter-Topic
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Expert System Examples 84
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Expert System Examples 85