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Knowledge-Based Systems Design. CONVENTIONAL SYSTEM LIFECYCLE. EXPERT SYSTEM LIFECYCLE. Problem recognition & feasibility studies. Problem recognition. User requirement. Preliminary requirement analysis and knowledge acquisition. Software requirement specification.

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Knowledge-Based Systems Design

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Problem recognition & feasibility studies

Problem recognition

User requirement

Preliminary requirement analysis

and knowledge acquisition

Software requirement specification

Selection of Expert System Tools


Software Design






Verification & validation





Operation and Maintenance

Operation and Maintenance

six step of esdlc

Problem identification and feasibility determination

Knowledge Acquisition (rapid prototyping)

Knowledge representation (rapid prototyping)

Verification and validation




Choose expert, identify technical resources, etc

Interviews, case studies, protocol analysis, brainstorming, etc

Production rules, predicate calculus, shells, semantic network, etc

Turing test, peer reviews


Postimplementation reviews

Six-Step of ESDLC
activities in the esdlc

Problem identification and feasibility determination

Knowledge Acquisition (rapid prototyping)

Knowledge representation (rapid prototyping)

Verification and validation



Knowledge Acquisition Activity

Seek out the champion

Locate a cooperative domain expert

Apply appropriate tools to tap the

experts knowledge

Represent the expert heuristic via prototyping

Verify the rules with the expert

Correct existing rules and add missing rules by working closely with the expert through rapid prototyping

Work with the user to ensure system acceptance and proper training

Meet with the expert and the user to determine procedures and content with maintaining and updating the system

Activities in the ESDLC
activities in the esdlc1
Problem identification and feasibility determination


Is present expertise going to be lost through retirement, transfer or departure to other firm

How much is the company spending to have present expertise solve the problem in question

Is the proposed ES needed in multiple locations

Is an expert available to help in building the ES

Does the problem domain require years of experience and cognitive reasoning to solve

Can the expert articulate how the problem will be solved

How critical is the proposed project

Are the task nonalgorithmic

Is a champion is the house

Activities in the ESDLC
knowledge acquisition1
Types of Knowledge

Procedural Rules, Strategies, Agendas, Procedures

Declarative Concepts, Objects, Facts

Meta-knowledge Knowledge about the other types of knowledge and how to use them

Heuristic Knowledge Rules of Thumb

Structural Knowledge Rule sets, Concept relationships, Concept to object relationships

Knowledge Acquisition
knowledge acquisition2
Sources of Knowledge

Expert Primary source of knowledge

End-user Valuable additional source of information

Multiple experts Additional experts to collect specialized knowledge of a sub-problem





Knowledge Acquisition
knowledge acquisition3
Knowledge Elicitation Task


Acquiring knowledge from expert

Require effective interpersonal communication skills

Able to obtain cooperation of the expert


Review the collected information and

identify the key pieces of knowledge

At first defining overall problem specification

Informal review of material

Establish the problem’s goal, constraints and scope

Later, use formal methods to interpret the different types of knowledge uncovered during the session.

Knowledge Acquisition
knowledge acquisition4
Knowledge Elicitation Task


Early, Identify the important concepts used by expert

Determine concept relationships and how the expert uses them to solve the problem

Later, look at more detail all the points


Form some new understanding of the problem that can aid further investigations

Guide the problem solving strategies

Knowledge Acquisition
knowledge acquisition5
Major Difficulties Knowledge Elicitation

Expert maybe unaware of the knowledge used

Expert may be unable to verbalize the knowledge

Expert may provide irrelevant knowledge

Expert may provide incomplete knowledge

Expert may provide inconsistent knowledge

Knowledge Acquisition
knowledge acquisition major elicitation task
Knowledge Engineer

Collect the knowledge

Interpret the knowledge

Analyze the knowledge

Coordinate project activities

Maintain cooperative effort


Provide problem overview

Help define interface

Help define explanation facility needs

Highlight areas that need developed

Aid in system testing

Help define in-place operation of system


Provide primary source of knowledge

Aid knowledge interpretation and analysis

Aid in system testing

Knowledge Acquisition: Major Elicitation Task
interviewing method
Direct Technique

Articulate knowledge by the domain expert

Includes interviews & case studies

Indirect Technique

Do not rely on the expert

Includes questionnaires

The most common knowledge elicitation technique in the design of expert systems is the interview method

managing the interview

Remove Fear

Remove Skepticism

Establish Reasonable Goals

Promote Openness to change

Provide Understanding of Expected Effort

Convey Importance of Involvement

preparing for the interview

Prepare Agenda

Key of good interview is have a clear objective of what you want to achieve

Establish the objective and agenda

Allows the team to understand and prepare


Set up the time and location of the interview session

At least one week notice

Meeting length to 1 hour

Don’t be late

Material List

Prepare a list of the materials you will use during the interview

Should be given to each group member

preparing for the interview1

Determine location of interview

Accommodate the expert

Choose a natural setting

Material List

Prepare a list of the materials you will use during the interview

Should be given to each group member

Record the interview, use 1 tape per session

Report content of each session

Bring coffee or cakes

beginning the interview
Establish a comfortable setting by discussing a topic of personal interest

Avoid an initial subject related to the project

Review the session’s objective and agenda

Resolve any misunderstanding and solicit suggestions

problem selection
Problem Selection
  • the development of an expert system should be based on a specific problem to be addressed by the system
  • it should be verified that expert systems are the right paradigm to solve that type of problem
    • not all problems are amenable to ES-based solutions
  • availability of resources for the development
    • experts/expertise
    • hardware/software
    • users
    • sponsors/funds
project management
Project Management
  • activity planning
    • planning, scheduling, chronicling, analysis
  • product configuration management
    • product management
    • change management
  • resource management
    • need determination
    • acquisition resources
    • assignment of responsibilities
    • identification of critical resources
es development stages
ES Development Stages
  • feasibility study
    • paper-based explanation of the main idea(s)
    • no implementation
  • rapid prototype
    • quick and dirty implementation of the main idea(s)
  • refined system
    • in-house verification by knowledge engineers, experts
  • field test
    • system tested by selected end users
  • commercial quality system
    • deployed to a large set of end users
  • maintenance and evolution
    • elimination of bugs
    • additional functionalities
error sources in es development
Error Sources in ES Development
  • knowledge errors
  • semantic errors
  • syntax errors
  • inference engine errors
  • inference chain errors
  • limits of ignorance errors
knowledge errors
Knowledge Errors
  • problem: knowledge provided by the expert is incorrect or incomplete
    • reflection of expert’s genuine belief
    • omission of important aspects
    • inadequate formulation of the knowledge by the expert
  • consequences
    • existing solution not found
    • wrong conclusions
  • remedy
    • validation and verification of the knowledge
      • may be expensive
semantic errors
Semantic Errors
  • problem: the meaning of knowledge is not properly communicated
    • knowledge engineer encodes rules that do not reflect what the domain expert stated
    • expert misinterprets questions from the knowledge engineer
  • consequences
    • incorrect knowledge, inappropriate solutions, solutions not found
  • remedy
    • formalized protocol for knowledge elicitation
    • validation of the knowledge base by domain experts
syntax errors
Syntax Errors
  • problem: rules or facts do not follow the syntax required by the tool used
    • knowledge engineer is not familiar with the method/tool
    • syntax not clearly specified
  • consequences
    • knowledge can’t be used
  • solutions
    • syntax checking and debugging tools in the ES development environment
inference engine errors
Inference Engine Errors
  • problem: malfunctions in the inference component of the expert system
    • bugs
    • resource limitations
      • e.g. memory
  • consequences
    • system crash
    • incorrect solutions
    • existing solutions not found
  • remedy
    • validation and verification of the tools used
inference chain errors
Inference Chain Errors
  • problem: although each individual inference step may be correct, the overall conclusion is incorrect or inappropriate
    • causes: errors listed above; inappropriate priorities of rules, interactions between rules, uncertainty, non-monotonicity
  • consequences
    • inappropriate conclusions
  • remedy
    • formal validation and verification
    • use of a different inference method
limits of ignorance errors
Limits of Ignorance Errors
  • problem: the expert system doesn’t know what it doesn’t know
    • human experts usually are aware of the limits of their expertise
  • consequences
    • inappropriate confidence in conclusions
    • incorrect conclusions
  • remedy
    • meta-reasoning methods that explore the limits of the knowledge available to the ES
expert systems and software engineering
Expert Systems and Software Engineering
  • software process models
    • waterfall
    • spiral
  • use of SE models for ES development
  • ES development models
    • evolutionary model
    • incremental model
    • spiral model
generic software process models
Generic Software Process Models
  • waterfall model
    • separate and distinct phases of specification and development
  • evolutionary development
    • specification and development are interleaved
  • formal systems development
    • a mathematical system model is formally transformed to an implementation
  • reuse-based development
    • the system is assembled from existing components

[Sommerville 2001]

waterfall model
Waterfall Model

[Sommerville 2001]

important concepts and terms

backward chaining

common-sense knowledge

conflict resolution

expert system (ES)

expert system shell


forward chaining


inference mechanism

If-Then rules


knowledge acquisition

knowledge base

knowledge-based system

knowledge representation

Markov algorithm


Post production system

problem domain

production rules


RETE algorithm


working memory

Important Concepts and Terms