Topic 4 Knowledge based Systems

							        Topic 4
Knowledge based Systems
                 Learning Objectives


• Describe the basic concepts in artificial intelligence.
• Understand the importance of knowledge in decision
  support.
• Examine the concepts of rule-based expert systems.
• Learn the architecture of rule-based expert systems.
• Understand the benefits and limitations of rule based
  systems for decision support.
• Identify proper applications of expert systems.
                            What is an ES?


• Expert System (ES) is a branch of
  Artificial Intelligence that attempt to
  mimic human experts.

     Expert systems can either
      support decision makers or
      completely replace them.

     Expert systems are the most
      widely applied & commercially
      successful AI technology.
                        What is an ES?

• Prof. Edward Feigenbaum of Stanford University, leading
  researchers in ES has produced the following definition:

   " . . . An intelligent computer program that uses knowledge
      and inference procedures to solve problems that are
      difficult enough to require significant human expertise for
      their solution."
       MYCIN: A medical expert system



 Developed at Stanford University in the mid 1970's.

 The first large expert system that perform at the
  level of human expert and use as benchmark by
  expert system developers.

 Provide consultative advise about bacteremia and
  meningitis.

 Bacteremia is an infections that involve bacteria in
  the blood.
      MYCIN: A medical expert system



 Meningitis is an infection which is an inflammation
  of the membranes that envelop the brain and spinal
  cord.
 Can be fatal, thus need quick response, but positive
  identification normally takes 24 - 48 hours.
 Normally doctors must begin treatment in the
  absence of lab results.
 Very complex and doctor need advice of an expert
  of bacteremia and meningitis.
            How MYCIN works?




 MYCIN begin by initiating a dialogue.
 Physician response to the questions
 MYCIN provides:
     a. diagnosis
     b. prescription
                  How MYCIN reasons?



 Laboratory results of body fluid analyses
 Symptoms that patient is displaying
 Characteristics of the patient such as age, sex etc.
                           MYCIN



MYCIN consultative proceeds in 2 phases:
 Diagnosis is made to identify the most likely infection
  organisms
 Prescribe one or more drugs (antibiotics)
           MYCIN: Example of a rule



IF      the stain of the organism is gram-positive
AND the morphology of the organism is coccus,
AND the growth conformation of the organism is
       clumps,
THEN
   there is suggestive evidence (0.7) that the identity of
   the organism is staphyloccus
             A consultation With MYCIN


1. Please enter information about the patient.
Name              Age                     Sex      Race
Sally             42 years                Female Melayu
2. Are there any positive cultures obtained from SALLY?
No
3. Are there any pending cultures of SALLY?
Yes
4. Site: date collected: stain examined: method
CSF 3-Jun-75                  Yes                 N/A
5. Stain of smear:
(type NONE if no organism was seen on the smear)
None

(Unable to reach conclusion from lab test, like physician continue
asking questions.)
      A consultation With MYCIN (cont..)


6. Has Sally recently had symptoms of persistent headache or other
   abnormal neurologic symptoms(dizziness, lethargy etc.)
   Yes

7. Has SALLY recently had objective evidence of abnormal neurologic
   signs documented by physician observation of examination?
   Yes

Questions 6 and 7 traditional approach to diagnose by symptoms.

Conclude from the questions by giving first conclusion.

Conclusion: The CSF cultures will be considered to be associated with
  MENINGITIS.
  A consultation With MYCIN (cont..)


The following questions will identify particular organism that cause
the infections.

8. Is the meningitis a hospital-acquired infection?
    Yes
9. Is SALLY clinical history consistent with EPIGLOTTITIS?
    No
10. Is Sally clinical history consistent with OTITIS-MEDIA?
    No
11. Has the patient ever been to an area that is endemic for
coccidiomycoses?
Unknown
12. Is Sally a burn patient?
Yes (Why)

Conclusion: The type of infection may be BACTERIAL.
    A consultation With MYCIN (cont..)


The final phase, prescription of antibiotics.

13. Does SALLY have clinically significant allergic reaction
  to any antimicrobial agent?
    No
14. Is Sally pregnant of breast feeding?
    No
15. Do you have reason to suspect that SALLY may have
  impaired renal functions?
    No
16. SALLY weight in kilograms?
    51.4
           A consultation With MYCIN


My preferred therapy recommendation is as follows:

Give the following combination:
1. ETHAMBUTAL
   Dose: 1.28g (13.0 100mg tablets) q24h PO for 60 days
    then 770 mg (7.5 100 mg tablets) q24h PO.
   Comments: periodic vision screening tests are
   recommended for optic neuritis.
2. INH
   Dose: 513 mg (5.0 100mg-tablets) q24h PO
3. RIFAMPIN
   Dose: 600 mg PO q24h
   Comments: Administer dose on empty stomach.
           HOW GOOD AN EXPERT IS MYCIN?



 In one complex evaluation, 8 independent evaluators
  evaluate MYCIN with 9 others human diagnostician for
  10 difficult cases of meningitis.

 The task used was the selection of drugs for cases of
  meningitis before causative agents had been identified.

 Two phases of the evaluation:
     a. MCYIN and 9 human experts evaluate 10 cases
     b. Each of them prescribe medications
          HOW GOOD AN EXPERT IS MYCIN?



Two evaluative criteria was used to see whether the
prescriptions:

    a. Would be effective against the actual bacteria
       after it was finally identified.

    b. Adequately covered for other possible bacteria
       while avoiding over-prescribing.
           HOW GOOD AN EXPERT IS MYCIN?




Results:

Criteria 1: MYCIN and 3 other humans expert
           consistently prescribe therapy that would have
           been effective for all 10 cases.

Criteria 2: MYCIN received higher ratings. 65% correct
           in all the cases whereas human expert 42.5%
           to 62.5%.
        HOW GOOD AN EXPERT IS MYCIN?


MYCIN strengths is based on 4 factors:

   a.   MYCIN's knowledge base is extremely detail because
        acquired from the best human practitioners.

   b.   MYCIN do not overlook anything or forget any details. It
        considers every possibility.

   c.   MYCIN never jumps to conclusions of fails to ask for key
        pieces of information.

   d.   MYCIN is maintained at a major medical center and
        consequently, completely current.

MYCIN represents 50 man-years of effort.
          CASE: GE Models Human
             Troubleshooters

• Problem:
• GE wanted an effective & dependable way of disseminating
  expertise to its engineers & preventing valuable knowledge from
  “retiring” from the company.

• Solution:
• GE decided to build an expert system that modeled the way a human
  troubleshooter works.
• The system builders spend several months interviewing an employee
  & transfer their knowledge to a computer.
• The new diagnostic technology enables a novice engineer to uncover
  a fault by spending only a few minutes at the computer terminal.
• Results:
• The system is currently installed at every railroad repair shop served
  by GE.
     Intelligent Systems in KPN Telecom
             and Logitech Vignette


• Problems in maintaining computers with varying
  hardware and software configurations
• Rule-based system developed
   Captures, manages, automates installation and
    maintenance
      Knowledge-based core
      User-friendly interface
      Knowledge management module employs natural language
       processing unit
               Artificial Intelligence


• Duplication of human thought process by
  machine
     Learning from experience
     Interpreting ambiguities
     Rapid response to varying situations
     Applying reasoning to problem-solving
     Manipulating environment by applying knowledge
     Thinking and reasoning
                           Artificial Intelligence
                              Characteristics

•   Symbolic processing
      Computers process numerically, people think symbolically
      Computers follow algorithms
           Step by step
      Humans are heuristic
           Rule of thumb
           Gut feelings
           Intuitive
•   Heuristics
      Symbols combined with rule of thumb processing
•   Inference
      Applies heuristics to infer from facts
•   Machine learning
        Mechanical learning
        Inductive learning
        Artificial neural networks
        Genetic algorithms
A.I: The Brief History of Time

2002                                     •Hybrid technology, Intelligent Agent,
                                         Collaborative Intelligence, Humanoid,
                                                   Sociable Machines

                              •Deep Blue beats human Chess master
1990              •Data Mining, Face Recognition, Decision Support System
           •Tutoring System, Fuzzy System, Commercial AI system
1980                      •Machine Learning, Speech Recognition

1970         •Machine Vision, Natural Language Processing
                            •Expert Systems
1951         •Dartmouth Conference - The Birth of AI
                 •The Turing Test
1940
                                •Neural Computation
        •1st Electronic Comp.
               •The Boolean Logic
1832   •The Birth of Analytical Engine
        Artificial Intelligence Concepts


• Expert systems
    Human knowledge stored on machine for use in problem-solving
• Natural language processing
    Allows user to use native language instead of English
• Speech recognition
    Computer understanding spoken language
• Sensory systems
    Vision, tactile, and signal processing systems
• Robotics
    Sensory systems combine with programmable electromechanical device
     to perform manual labor
        Artificial Intelligence Concepts


• Vision and scene recognition
    Computer intelligence applied to digital information from machine
• Neural computing
    Mathematical models simulating functional human brain
• Intelligent computer-aided instruction
    Machines used to tutor humans
       Intelligent tutoring systems
• Game playing
    Investigation of new strategies combined with heuristics
        Artificial Intelligence Concepts


• Language translation
    Programs that translate sentences from one language to another
     without human interaction
• Fuzzy logic
    Extends logic from Boolean true/false to allow for partial truths
    Imprecise reasoning
    Inexact knowledge
• Genetic algorithms
    Computers simulate natural evolution to identify patterns in sets of data
• Intelligent agents
    Computer programs that automatically conduct tasks
          Categories of Knowledge Based
                      System


•   KBS is a computer system which embodies knowledge
    about a specific problem domain and can thus be used
    to apply this knowledge to solve problems from that
    problem domain
      Categories of Knowledge Based
                  System

•   KBS includes:
      Expert systems
      Intelligent database systems: usually take the
       form of intelligent front end
      Intelligent tutoring system: attempt to model a
       human tutor
      Intelligent CASE tools: replicate knowledge of
       software engineer, integrating CASE and KBS
      Integrated of hybrid system: integrating KBS
       with traditional information system
                         Experts


• Experts
    Have special knowledge, judgment, and experience
    Can apply these to solve problems
       Higher performance level than average person
       Relative
       Faster solutions
       Recognize patterns
• Expertise
    Task specific knowledge of experts
       Acquired from reading, training, practice
              Expert Systems Features


• Expertise
    Capable of making expert level decisions
• Symbolic reasoning
    Knowledge represented symbolically
    Reasoning mechanism symbolic
• Deep knowledge
    Knowledge base contains complex knowledge
• Self-knowledge
    Able to examine own reasoning
    Explain why conclusion reached
        Applications of Expert Systems


• DENDRAL project
    Applied knowledge or rule-based reasoning commands
    Deduced likely molecular structure of compounds
• MYCIN
    Rule-based system for diagnosing bacterial infections
• XCON
    Rule-based system to determine optimal systems configuration
• Credit analysis
    Ruled-based systems for commercial lenders
• Pension fund adviser
    Knowledge-based system analyzing impact of regulation and
     conformance requirements on fund status
                              Applications


• Finance
    Insurance evaluation, credit analysis, tax planning, financial planning
     and reporting, performance evaluation
• Data processing
    Systems planning, equipment maintenance, vendor evaluation, network
     management
• Marketing
    Customer-relationship management, market analysis, product planning
• Human resources
    HR planning, performance evaluation, scheduling, pension
     management, legal advising
• Manufacturing
    Production planning, quality management, product design, plant site
     selection, equipment maintenance and repair
                             Environments


•   Consultation (runtime)
•   Development
        Major Components of Expert
                 Systems


• Major components
   Knowledge base
     Facts
     Special heuristics to direct use of knowledge
   Inference engine
     Brain
     Control structure
     Rule interpreter
   User interface
     Language processor
      Additional Components of Expert
                  Systems

• Additional components
    Knowledge acquisition subsystem
       Accumulates, transfers, and transforms expertise to computer
    Workplace
       Blackboard
       Area of working memory
       Decisions
         • Plan, agenda, solution
    Justifier
       Explanation subsystem
         • Traces responsibility for conclusions
    Knowledge refinement system
       Analyzes knowledge and use for learning and improvements
           Knowledge Presentation


• Production rules
   IF-THEN rules combine with conditions to produce
    conclusions
   Easy to understand
   New rules easily added
   Uncertainty
• Semantic networks
• Logic statements
                    Inference Engine


• Forward chaining
   Looks for the IF part of rule first
   Selects path based upon meeting all of the IF
    requirements
• Backward chaining
     Starts from conclusion and hypothesizes that it is true
     Identifies IF conditions and tests their veracity
     If they are all true, it accepts conclusion
     If they fail, then discards conclusion
                Conventional and ES


       Conventional Systems                        Expert Systems

Knowledge and processing are            Knowledge base is clearly separated
combined in one sequential program      from the processing (inference)
                                        mechanism (knowledge rules are
                                        separated from the control)
Programs do not make mistakes (only     Program may make mistakes.
programmers do)

Do not usually explain why input data   Explanation is a part of most expert
are needed or how conclusions were      systems
drawn
The system operates only when it is     The system can operate with only a
completed                               few rules (as a first prototype)

Execution is done on a step-by-step     Execution is done by using heuristics
(algorithmic) basis                     and logic
                 Conventional and ES


       Conventional Systems                      Expert Systems

Needs complete information to operate Can operate with incomplete or
                                      uncertain information

Effective manipulation of large       Effective manipulation of large
databases                             knowledge bases

Representation and use of data        Representation and use of knowledge


Efficiency is a major goal            Effectiveness is a major goal


Easily deals with quantitative data   Easily deals with qualitative data
         General Problems Suitable for
               Expert Systems

• Interpretation systems
    Surveillance, image analysis, signal interpretation
• Prediction systems
    Weather forecasting, traffic predictions, demographics
• Diagnostic systems
    Medical, mechanical, electronic, software diagnosis
• Design systems
    Circuit layouts, building design, plant layout
• Planning systems
    Project management, routing, communications, financial plans
       General Problems Suitable for Expert
                     Systems


• Monitoring systems
    Air traffic control, fiscal management tasks
• Debugging systems
    Mechanical and software
• Repair systems
    Incorporate debugging, planning, and execution capabilities
• Instruction systems
    Identify weaknesses in knowledge and appropriate remedies
• Control systems
    Life support, artificial environment
         Participants in ES Development



•   The main participants in the process of building an
    expert system are:
      a. the domain expert
      b. the knowledge engineer
      c. the user.
             Participants in ES Development



•     THE DOMAIN EXPERT
       Is a person who has the special knowledge, judgment,
        experience, skills and methods, to give advice and solve
        problems in a manner superior to others.

       Although an expert system usually models one or more experts,
        it may also contain expertise from other sources such as books
        and journal articles.

       Qualifications needed by the Domain Expert:
           Has expert knowledge
           Has efficient problem-solving skills
           Can communicate the knowledge
           Can devote time
           Must be cooperative
            Participants in ES Development



•   THE KNOWLEDGE ENGINEER
       A person who designs, builds and tests an expert systems.

     Qualifications needed by Knowledge Engineer:

            Has knowledge engineering skills (art of building expert
             system)
            Has good communications skills
            Can match problems to software
            has expert system programming skills
         Participants in ES Development



•   THE USER
     Is a person who uses the expert system once it is developed.

     Can aid in knowledge acquisition (giving broad understanding
      of the problems)

     Can aid in system development
          When to Use Expert Systems


   Provide a high potential payoff or significantly reduced
    downside risk
   Capture and preserve irreplaceable human expertise
   Provide expertise needed at a number of locations at the
    same time or in a hostile environment that is dangerous
    to human health
          When to Use Expert Systems


   Provide expertise that is expensive or rare
   Develop a solution faster than human experts can
   Provide expertise needed for training and development
    to share the wisdom of human experts with a large
    number of people
  ES Development Life Cycles



                    Phase 1            Reformulations
                   Assessment
Requirements
                     Phase 2           Explorations
               Knowledge Acquisition
 Knowledge
                     Phase 3           Refinements
                     Design
 Structure
                     Phase 4
                       Test
Evaluation
                    Phase 5
                  Documentation
  Product
                    Phase 6
                   Maintenance
             Benefits of Expert Systems


•   Increased outputs
•   Increased productivity
•   Decreased decision-making time
•   Increased process and product quality
•   Reduced downtime
•   Capture of scarce expertise
•   Flexibility
•   Ease of complex equipment operation
•   Elimination of expensive monitoring equipment
•   Operation in hazardous environments
•   Access to knowledge and help desks
            Benefits of Expert Systems


•   Ability to work with incomplete, imprecise, uncertain data
•   Provides training
•   Enhanced problem solving and decision-making
•   Rapid feedback
•   Facilitate communications
•   Reliable decision quality
•   Ability to solve complex problems
•   Ease of knowledge transfer to remote locations
•   Provides intelligent capabilities to other information
    systems
                              Limitations


• Knowledge not always readily available
• Difficult to extract expertise from humans
      Approaches vary
      Natural cognitive limitations
      Vocabulary limited
      Wrong recommendations
• Lack of end-user trust
• Knowledge subject to biases
• Systems may not be able to arrive at conclusions
                  Success Factors


•   Management champion
•   User involvement
•   Training
•   Expertise from cooperative experts
•   Qualitative, not quantitative, problem
•   User-friendly interface
•   Expert’s level of knowledge must be high
               Types of Expert Systems


• Rule-based Systems
    Knowledge represented by series of rules
• Frame-based Systems
    Knowledge represented by frames
• Hybrid Systems
    Several approaches are combined, usually rules and frames
• Model-based Systems
    Models simulate structure and functions of systems
• Off-the-shelf Systems
    Ready made packages for general use
• Custom-made Systems
    Meet specific need
• Real-time Systems
    Strict limits set on system response times
        Using Expert Systems on the Net




• The widespread availability and use of the Internet and
  intranets now provide the opportunity to disseminate
  expertise and knowledge to mass audiences.

• ESs can be transferred over the Net not only to human
  users, but also to other computerized systems,
  including DSS, robotics, and databases.
        Using Expert Systems on the Net




• The Web also can support the spread of multimedia-
  based expert systems.

    Such systems, referred to as Intellimedia Systems,
     support the integration of extensive multimedia
     applications and ES.

    For example in tourism industry and remote
     equipment-failure diagnosis
• WEBCANDI
                    Managerial Issues


 Cost-benefit and justification.
  While some of the benefits of
  intelligent systems are
  tangible, it is difficult to put a
  dollar value on the intangible
  benefits of many intelligent
  systems.


 Heightened expectations.              Acquiring knowledge.
  When there is too much                 Intelligent systems are built up
  expectation and hope                   on experts’ knowledge. How
  associated with intelligent            can an expert be motivated to
  technologies, management               contribute his or her
  may get discouraged.                   knowledge?
                 Managerial Issues (cont.)


 System acceptance. The             Embedded technologies.
  acceptance of intelligent           Intelligent systems are
  systems by the IS department        expected to be embedded in
  and the integration of such         at least 20 % of all IT
  systems with mainstream IT is       applications in about ten
  a critical success factor.          years.

 System integration. Intelligent    Ethical issues. Finally, there
  systems can succeed as              are several issues related to
  standalone systems, but they        the use of intelligent systems.
  have a broader area of              The actions performed by an
  applications when integrated        ES can be unethical, or even
  with other computer-based           illegal. There is also the issue
  information systems.                of using knowledge extracted
                                      from people and replacing
                                      people with machines.