Expert Systems

1. Artificial Inteligence
Hande TETİK
2. Expert Systems
Aslı YAZAĞAN
Hande TETİK
IS533 DECISION SUPPORT SYSTEMS Hande Tetik & Aslı Yazağan

Overview
Artificial Intelligence Field
Concepts in ES – Expert and Expert Systems
Structure of Expert Systems
How Expert Systems work
Categories of Expert Systems
Knowledge – Based Systems vs. Expert Systems
Expert Systems Success Factor
Types of Expert Systems
Benefit of Expert Systems
Problem and Limitations of Expert Systems
Expert Systems on the WEB
ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS

Artificial intelligence (AI)
A subfield of computer science, concerned with symbolic
reasoning and problem solving
AI has many definitions…
Behavior by a machine that, if performed by a human
being, would be considered intelligent
“…study of how to make computers do things at which, at
the moment, people are better
Theory of how the human mind works
1. ARTIFICIAL INTELLIGENCE

AI pioneers
• Regarded as a father of AI
• The Darthmouth summer research
project on AI (1956)
• «Making a machine behave in ways that
would be called intelligent if a human were
so behaving»

AI Objectives
• Make machines smarter (primary goal)
• Understand what intelligence is
• Make machines more intelligent and useful
Signs of intelligence
• Learn or understand from experience
• Make sense out of ambiguous situations
• Respond quickly to new situations
• Use reasoning to solve problems
• Apply knowledge to manipulate the environment
Questions / Answers

Symbolic Processing
Represents knowledge as a set of symbols, and
AI Uses these symbols to represent problems, and
Apply various strategies and rules to manipulate symbols to solve problems
A symbol is a string of characters that stands for some real-world concept (e.g., Product,
consumer,…)
Examples:
(DEFECTIVE product)
(LEASED-BY product customer) - LISP
Tastes_Good (chocolate)

AI Concepts
Reasoning
Inferencing from facts and rules using heuristics or other search approaches
Pattern Matching
Attempt to describe and match objects, events, or processes in terms of their qualitative
features and logical and computational relationships
Knowledge Base
Computer
Inference
Capability
Knowledge
Base
INPUTS
(questions,
problems, etc.)
OUTPUTS
(answers,
alternatives, etc.)

Evolution of artificial intelligence
Time
ComplexityoftheSolutions
Naïve
Solutions
General
Methoids
Domain
Knowledge
Hybrid
Solutions
Embedded
Applications
1960s 1970s 1980s 1990s 2000+
Low
High

Artificial vs. Natural Intelligence
Advantages of AI
 More permanent
 Ease of duplication and dissemination
 Less expensive
 Consistent and thorough
 Can be documented
 Can execute certain tasks much faster
 Can perform certain tasks better than many people
Advantages of Biological Natural Intelligence
 Is truly creative
 Can use sensory input directly and creatively
 Can apply experience in different situations

AI Field
 Provides the scientific foundation for many commercial technologies
 AI is many different sciences and technologies
 It is a collection of concepts and ideas
Psychology
Philosophy
Logic
Sociology
Human Cognition
Linguistics
Neurology
Mathematics
Management Science
Information Systems
Statistics
Engineering
Robotics
Biology
Human Behavior
Pattern Recognition
Voice Recognition
Intelligent tutoring
Expert Systems
Neural Networks
Natural Language Processing
Intelligent Agents
Fuzzy Logic
Game Playing
Computer Vision
Automatic Programming
Genetic Algorithms
Machine Learning
Autonomous Robots
Speech Understanding
The AI
Tree
Computer Science
DisciplinesApplications

AI Areas
Major
• Expert Systems
• Natural Language Processing
• Speech Understanding
• Robotics and Sensory Systems
• Computer Vision and Scene Recognition
• Intelligent Computer-Aided Instruction
• Automated Programming
• Neural Computing Game Playing
Additional
• Game Playing, Language Translation
• Fuzzy Logic, Genetic Algorithms
• Intelligent Software Agents

• AI is a common technology both science fiction and projections about the
future of technology& society
• The impact of AI on society is a serious area of study for futurists
ROBOCOP
FRIEND

What is an Expert ?
An expert is one has ability to use
skill
experience
knowledge
efficiently
to solve a problem using
tricks, shortcuts, and rules-of-thumb.
2. EXPERT SYSTEMS

Which one is an Expert on web search?
2. EXPERT SYSTEMS

Who knows the best treatment for you?
2. EXPERT SYSTEMS

Who is an Expert on music?
2. EXPERT SYSTEMS

Which one is an Expert on cooking?
2. EXPERT SYSTEMS

Consulting and Consultants
What are the attributes of effective consultants and consulting?
Consulting is goal oriented
A good consultant is efficient
Good consultants justify their recommendations by explaining their reasoning
Consultants are able to work with imperfect information
A consultation is adaptive
2. EXPERT SYSTEMS

Consultants and Consulting Example
2. EXPERT SYSTEMS
GOAL
ORIENTED
EFFICIENT

2. EXPERT SYSTEMS
ADAPTIVE
IMPERFECT
INFORMATION
EXPLAIN
REASONING

Expert Systems
- Expert systems represent a practical application of artificial intelligence (AI)
research.
- Attempts to imitate expert’s reasoning processes and knowledge in solving specific
problems
- ES do not replace experts.
Make their knowledge and experience more widely available,
Permit non-experts to work better
2. EXPERT SYSTEMS
1.Generation
• If-then rules
2.Generation
• Flexible in adopting
multiple knowledge
representation and
reasoning method (
Neural Network )

Why Expert Systems?
 A tool for preserving the professional knowledge that is crucial to competitiveness
 A tool for documenting professional knowledge for examination or improvement
 A tool for training new employees and disseminating knowledge
 A tool to transfer knowledge more easily at lower cost
Transfering Expertise
 acquisition
 representation
 inferencing
 transfer
2. EXPERT SYSTEMS

Structure of Expert Systems
2. EXPERT SYSTEMS
Knowledge
Engineer
Human
Expert(s) Other Knowledge
Sources
Knowledge
Elicitation
Information
Gathering
Knowledge
Base(s)
(Long Term)
Rule
Firings
Knowledge
Rules
Inferencing
Rules
Data-information
predictions
relations-consequences

Structure of Expert Systems | Inference Engine
The brain of ES system. Interprets rules and draw conclusions.
Inference is the process of chaining multiple rules together based on available data
- If the expert first collect data then infer from it => Forward Chaining
- If the expert starts with a hypothetical solution
and then attempts to find facts to prove it => Backward Chaining
2. EXPERT SYSTEMS
 Forward Chaining
• Data driven
• When: If all facts available up front.
• If  then
 Backward Chaining
• Goal Driven
• When: there are many
attributes employed in many
rules (e.g diagnostic problems )
• Then  If

Inference Engine| Forward Chaning Example
2. EXPERT SYSTEMS
QUESTION: What is the diagnosis?
• R2 fires, adding (nasal congestion) to
working memory.
• R4 fires, adding (fever) to working
memory.
• R5 fires, adding (achiness) to working
memory.
• R6 fires, adding (viremia) to working
memory.
• R1 fires, diagnosing the disease as
(influenza) and exits, returning the
diagnosis
Source: http://ai-depot.com/Tutorial/RuleBased-Conclusion.html

Inference Engine | Backward Chaning Example
2. EXPERT SYSTEMS
QUESTION: Is the diagnosis Influenza ?
• R1 fires since the goal, diagnosis(influenza), matches the
conclusion of that rule. New goals are created: (nasal
congestion) and (viremia) and backchaining is recursively
called with these new goals.
• R2 fires, matching goal nasal congestion. New goal is
created: (runny nose). Backchaining is recursively called.
Since (runny nose) is in working memory, it returns true.
• R6 fires, matching goal viremia. Back-chaining recursion with
new goals: (fever), (achiness) and (cough)
• R4 fires, adding goal (temperature > 100). Since
(temperature = 101.7) is in working memory, it returns true.
• R3 fires, adding goal (body-aches). On recursion, there is no
information in working memory nor rules that match this
goal. Therefore it returns false and the next matching rule is
chosen. That rule is R5 which fires, adding goal (headache).
Since (headache) is in working memory, it returns true.
• Goal (cough) is in working memory, so that returns true.
• Now, all recursive procedures have returned true, the system
exits, returning true: this hypothesis was correct: subject has
influenza.
Source: http://ai-depot.com/Tutorial/RuleBased-Conclusion.html

Categories for Expert Systems
2. EXPERT SYSTEMS
Category Problem Adressed
Interpretation Inferring situation descriptions from observations
Prediction Inferring likely consequences of given situation
Diagnosis Inferring system malfunctions from observation
Design Configuring objects under constraints
Planning Developing plans to achieve the goals
Monitoring Comparing observations to plans, flagging exceptions
Debugging Prescribing remedies for malfunctions
Repair Executing a plan to administer a remedy
Instruction Diagnosing, debugging, and correcting student performance
Control Interpreting, predicting, repairing and monitoring system behaivors

Knowledge Based Systems vs. Expert Systems
• Expert system makes decision and solves problems using knowledge and analytical rules
defined by experts in that field.
• Knowledge System performs tasks using knowledge that do not really need an expert.
Can be constructed more quickly and cheaply than Expert Systems.
2. EXPERT SYSTEMS
Knowledge Base
Expert
Systems

Expert Systems Success Factor
 The task must be clearly defined.
 Test case examples should be available.
Expert systems are built to solve problems that have been solved before:
Documented test cases will provide a list of the factor present each time the
problem was solved along with the solution.
The advising task should have a verbal orientation.
If the problem environment requires extensive visual reference and
graphical information that cannot be described verbally, it might very
difficult to implement an expert system e.g : architecture
2. EXPERT SYSTEMS

Types of Expert Systems
2. EXPERT SYSTEMS
Rule-based ES. Knowledge is represented by a series of rules.
Frame-based Systems. Knowledge is represented as a series of frames (an object-oriented
approach).
Hybrid Systems. Involve several approaches such as fuzzy logic and neural networks.
Model-based Systems. Structured around a model that simulates the structure and function
of the system under study.
Ready-made Systems. Utilize prepackaged software.
Real-time Systems. Systems designed to produce a just-in-time response.

Benefits of Expert Systems
 Capture Scarce Expertise
 Increased Productivity and Quality
 Decreased Decision Making Time
 Reduced Downtime via Diagnosis
 Easier Equipment Operation
 Elimination of Expensive Equipment
 Ability to Solve Complex Problems
 Knowledge Transfer to Remote Locations
 Integration of Several Experts' Opinions
 Can Work with Uncertain Information
 Etc.
2. EXPERT SYSTEMS

Problems and Limitations of ES
 Knowledge is not always readily available
 Expertise can be hard to extract from humans
• Fear of sharing expertise
• Conflicts arise in dealing with multiple experts
 ES work well only in a narrow domain of knowledge
 Experts’ vocabulary often highly technical
 Knowledge engineers are rare and expensive
 Lack of trust by end-users
 ES sometimes produce incorrect recommendations
2. EXPERT SYSTEMS

2. EXPERT SYSTEMS
ES Critical Success Factors
• Having a Champion in Management
• User Involvement and Training
• Justification of the Importance of the Problem
• Good Project Management
• The level of knowledge must be sufficiently high
• There must be (at least) one cooperative expert
• The problem must be mostly qualitative
• The problem must be sufficiently narrow in scope
• The ES shell must be high quality, with friendly user interface, and naturally store
and manipulate the knowledge

2. EXPERT SYSTEMS
• Only about 1/3 survived more than five years
• Generally ES failed due to managerial issues
– Lack of system acceptance by users
– Inability to retain developers
– Problems in transitioning from development to maintenance (lack of refinement)
– Shifts in organizational priorities
• Proper management of ES development and deployment could resolve most of
them
Longevity of Commercial ES

2. EXPERT SYSTEMS
Applications of Expert Systems
DENDRAL: Used to identify the
structure of chemical compounds.
First used in 1965
LITHIAN: Gives advice to
archaeologists examining
stone tools

PROSPECTOR:
Used by geologists to
identify sites for drilling or
mining
PUFF:
Medical system
for diagnosis of respiratory
conditions
Applications of Expert Systems

Expert Systems on the Web
2. EXPERT SYSTEMS
http://www.aiinc.ca/http://www.vanguardsw.com
http://www.expertise2go.com

2. EXPERT SYSTEMS
An ES Consultation with ExSys
• Founded in 1983
• Longest-lived knowledge automation
expert system software company
in the industry
ExSys

2. EXPERT SYSTEMS
Example of Application Areas:
Dog Breeding Advisor
Considers Various Factors
• Suitability with small children
• Exercise and grooming requirements
• Look or size of the dog
Results

2. EXPERT SYSTEMS
Exsys Corvid Case Studies

2. EXPERT SYSTEMS
• Transportation waterways and streams are important
• Preventation of destructive erosion, scour, and lateral migration taken into account
• Salix Applied Earthcare used Exsys Corvid®to develop a knowledge automation expert
system named Greenbank to address this need.
• 44 channel and bank protection procedures were identified and incorporated into the Exsys
Corvid system, which recommends the best techniques for particular situations.

Who is responsible if the advice is wrong?
• The user?
• The domain expert?
• The knowledge engineer?
• The programmer of the expert system shell?
• The company selling the software?
Legal and Ethical Issues

Expert Systems

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Expert Systems