© 2002-9 Franz J. Kurfess Expert System Examples 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz...

© 2002-9 Franz J. Kurfess Expert System Examples 1

CPE/CSC 481: Knowledge-Based Systems

CPE/CSC 481: Knowledge-Based Systems

Dr. Franz J. Kurfess

Computer Science Department

Cal Poly


Usage of the SlidesUsage 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

([email protected]) I usually put together a subset for each quarter as a

“Custom Show” to view these, go to “Slide Show => Custom Shows”, select the

respective quarter, and click on “Show” To 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


Course OverviewCourse Overview Introduction Knowledge Representation

Semantic Nets, Frames, Logic

Reasoning and Inference Predicate Logic, Inference

Methods, Resolution

Reasoning with Uncertainty Probability, Bayesian Decision

Making

Expert System Design ES Life Cycle

CLIPS Overview Concepts, Notation, Usage

Pattern Matching Variables, Functions,

Expressions, Constraints

Expert System Implementation Salience, Rete Algorithm

Expert System Examples Conclusions and Outlook


Overview ES ExamplesOverview ES Examples

Motivation Objectives Chapter Introduction

Review of relevant concepts Overview new topics Terminology

R1/XCON System Configuration Knowledge Representation Reasoning

MYCIN

Human Resources ES OSHA Hazard Awareness

Advisor Gensym G2 Real-Time

Expert System Important Concepts and

Terms Chapter Summary


LogisticsLogistics Introductions Course Materials

textbooks (see below) lecture notes

PowerPoint Slides will be available on my Web page handouts Web page

http://www.csc.calpoly.edu/~fkurfess

Term Project Lab and Homework Assignments Exams Grading


Bridge-InBridge-In


Pre-TestPre-Test


MotivationMotivation

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


ObjectivesObjectives 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


R1/XCONR1/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


System ConfigurationSystem Configuration complexity

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 components

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,


Knowledge RepresentationKnowledge Representation configuration space

constructed 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 knowledge retrieved from the external data base as needed

control knowledge rules that govern the sequence of subtasks

constraint knowledge rules that describe properties of partial configurations


Example ComponentExample Component partial description of RK611* disk controller facts are retrieved from the data base and then stored in

templates

RK611* Class: UniBus module Type: disk drive Supported: yes Priority Level: buffered NPR Transfer Rate: 212 . . .


Example RuleExample 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


Configuration TaskConfiguration 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


ReasoningReasoning 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 appropriate rules for particular situations

execution control a substantial portion of the rules are used to determine

what to do next groups of rules are arranged into subtasks


Performance EvaluationPerformance 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


Development of R1/XCONDevelopment 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


Extension of R1/XCONExtension of R1/XCON addition of new knowledge

wider class of data additional computer system families

new components refined subtasks more detailed descriptions of subtasks revised descriptions for performance or systematicity reasons

extended task definition configuration of ``clusters''

tightly interconnected multiple CPUs

related system XSEL tool for sales support


Summary R1/XCONSummary 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 control knowledge the availability of enough knowledge about what to do next was critical

for the performance and eventual success of the system

suitability of rule-based systems appropriate vehicle for the encoding of expert knowledge subject to a good selection of application domain and task


MYCINMYCIN

based on a presentation by

Adam Gray, CSC 481 W04

some modifications by Franz J. Kurfess, W05, W06

A. Gray, 2004


OverviewOverview History

DENDRAL MYCIN

Background Knowledge Representation Knowledge Manipulation Uncertainty Performance Evaluation Advantages and Problems References

A. Gray, 2004


DENDRALDENDRAL 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

A. Gray, 2004


MYCIN BackgroundMYCIN 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 non-

specialized doctors

A. Gray, 2004


MYCIN ImplementationMYCIN Implementation Goal-directed system that uses a basic backward-

chaining 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

A. Gray, 2004


Example RuleExample 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

A. Gray, 2004


RepresentationRepresentation

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 “object-parameter-value” triples “culture” would be an object “site” would be a parameter of “culture” a possible value of this parameter would be “blood”

A. Gray, 2004


ManipulationManipulation

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 tries to see if the disease is known if it isn’t begins reasoning process

Basic routine in MYCIN attempt to find the value of a parameter

A. Gray, 2004


Finding ValuesFinding 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

A. Gray, 2004


UncertaintyUncertainty

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

A. Gray, 2004


Certainty FactorsCertainty 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

A. Gray, 2004


Performance EvaluationPerformance 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

A. Gray, 2004


ControlsControls Judges’ bias for/against computers

Judges did not know who recommended each therapy

Difficulty of problems 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

A. Gray, 2004


Good PointsGood 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

A. Gray, 2004


DifficultiesDifficulties

Narrow in scope did not scale up well to larger problems

Practical concerns Doctors have reservations about advise from computers Legal issues

A. Gray, 2004


ReferencesReferences 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.

A. Gray, 2004


Human Resources Expert SystemHuman 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/WorkingForGover

nment/HRExpert.htm some functionality limited to authorized users

developed by Softlaw Corporation http://www.softlaw.com.au


ObjectivesObjectives

improve HR advice and information quality, consistency, timeliness

enable value-adding strategic functions e.g. work force planning

extend use of technology from transaction-based ES to advice and information systems


HR Expert PrinciplesHR Expert Principles

enhanced electronic decision tree on-line inquiries from users determine branches accessible via official HR web sites integrated with source documents

legislation, personnel handbook, etc.


HR Expert: InquiriesHR Expert: Inquiries


HR Expert: Service HistoryHR Expert: Service History


OutputOutput

summary screens reports letters applications and forms audit reports


HR Expert: Summary ReportHR Expert: Summary Report


HR Expert: Full ReportHR Expert: Full Report


ProjectProject phase 1 - pilot project

3 agencies, 1,250 staff, conducted in 2002-3 demonstrated potential savings, user satisfaction, qualitative benefits

phase 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


BenefitsBenefits 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 Resources direct access to information

about entitlements less tedious work

e.g. looking up information when employees need it

reduced need for repetitive work

more consistent decisions on-demand generation of

reports standardized reports


IssuesIssues

some of the input provided by the users not always accurate, up to date

only generic conditions and entitlements special cases not included

limited coverage not all laws and regulations included

requires computer and Web access commitment and buy-in from staff and employees


StatusStatus operational and in use

update to include recent changes in laws and regulations under way current 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


RuleBurst DemoRuleBurst 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


ReferencesReferences 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/WorkingForGovernment/HRExpert.htm

Australian Government Information Management Office Report at http://www.agimo.gov.au/publications/2004/05/egovt_challenges/accountability/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&infopageid=152

RuleBurst KB Development Environment http://www.ruleburst.com/

sites visited 03-02-05, 02-28-06


OSHA Hazard Awareness AdvisorOSHA 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


ObjectivesObjectives

to help identify and understand common safety and health hazards in the work place especially aimed at small businesses

designed for beginners may be useful for experts as well

widely available through online and downloadable versions downloadable version only for MS Windows


LimitationsLimitations

may not identify all hazards will not determine compliance with OSHA standards not a substitute for safety and health professionals


OSHA PrinciplesOSHA Principles

expert system technology accessible via official OSHA web sites integrated with source documents

standards, legislation, etc.


Hazard Awareness Advisor InquiriesHazard Awareness Advisor Inquiries


Hazard Awareness Advisor ReportHazard Awareness Advisor Report

highlights details


Report HighlightsReport 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.


OSHA Report Detail: Portable Ladders 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.


ProjectProject preceded by other SHA advisors

Asbestos in '95 Confined Spaces in '96 Fire Safety in '97 Lead in Construction in '98

input 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


BenefitsBenefits 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

employees identification of potential

hazards improved working conditions possible compliance issues

standards and regulations


IssuesIssues

all of the input provided by the users not always accurate, up to date

limited coverage may not identify all hazards


StatusStatus version 1.0 operational and in use since September 1999

update to include recent changes in laws and regulations ???


Other OSHA Expert AdvisorsOther 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


ReferencesReferences 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


Gensym G2Gensym 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 by additional modules


Gensym G2 PlatformGensym G2 Platform


Gensym G2 UseGensym G2 Use

http://www.gensym.com/images/pages/g2platform.jpg


Gensym Development CycleGensym Development Cycle

http://www.gensym.com/images/pages/xtreme-programming-lifecycle.jpg


Ericsson Network Management System

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


Ericsson FMXEricsson FMX largely automated system for wireless network management

concentration on the fault management process reacts to all identified events very quickly

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


FMX ScreenshotFMX Screenshot

Input

Condition

DecisionOutputs

http://www.gensym.com/?p=success_stories&id=13


Dow Chemicals Closed Loop Optimizer

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


Energy Management with G2Energy 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


Seadrift Functional DiagramSeadrift Functional Diagram


Seadrift ScreenshotSeadrift Screenshot


Seadrift OutcomeSeadrift 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 larger potential for savings

extension to other components and systems in the plant more sophisticated modeling usage for other plans

user satisfaction operators were skeptical initially, but accepted and used

the system very quickly better view of overall plant operations


ReferencesReferences

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/index.htm


QuestionsQuestions


Figure ExampleFigure Example


Post-TestPost-Test


Important Concepts and TermsImportant 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

knowledge base knowledge-based system knowledge representation Markov algorithm matching Post production system problem domain production rules reasoning RETE algorithm rule working memory


Summary Chapter-TopicSummary Chapter-Topic

© 2002-9 Franz J. Kurfess Expert System Examples 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz...

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