Download Specific expert systems

Introduction to
Artificial Intelligence
Lecture # 3
Expert system
•
•
•
•
Expert system is an artificial intelligence program that has expert-level
knowledge about a particular domain and knows how to use its knowledge to
respond properly.
Domain refers to the area within which the task is being performed. Ideally the
expert systems should substitute a human expert.
Edward Feigenbaum of Stanford University has defined expert system as “an
intelligent computer program that uses knowledge and inference procedures to
solve problems that are difficult enough to require significant human expertise for
their solutions.”
It is a branch of artificial intelligence introduced by researchers in the Stanford
Heuristic Programming Project.
•
•
•
•
•
Expert system
The expert systems is a branch of AI designed to work within a particular
domain.
As an expert is a person who can solve a problem with the domain
knowledge in hands it should be able to solve problems at the level of a
human expert.
The source of knowledge may come from a human expert and/or from
books, magazines and internet.
As knowledge play a key role in the functioning of expert systems they are
also known as knowledge-based systems and knowledge-based expert
systems.
The expert’s knowledge about solving the given specific problems is called
knowledge domain of the expert.
•
•
•
So,
ES is a computer application that performs a task that would otherwise
be performed by a human expert.
For example, there are expert systems that can diagnose human illnesses,
make financial forecasts, and schedule routes for delivery vehicles. Some
expert systems are designed to take the place of human experts, while
others are designed to aid them.
To design an expert system, one needs a knowledge engineer, an
individual who studies how human experts make decisions and translates
the rules into terms that a computer can understand.
PERFORMANCE
Performance of the expert system is based on following methods:-
►Knowledge engineering:►Building
an expert
ENGINEERING.
system
is
known
as
KNOWLEDGE
►In this knowledge gathers from subject matter experts
codifying this knowledge according to the formalism.
►Persons doing this are called KNOWLEDGE ENGINEER.
and then
COMPONENTS OF EXPERT SYSTEMS
COMPONENTS OF EXPERT SYSTEMS
An expert system is divided into two sub-systems: knowledge base ,
reasoning/inference engine and rules.
•
•
•
The knowledge base represents facts and rules.
The inference engine applies the rules to the known facts to deduce new
facts.
And rule is a conditional statement that links given conditions to actions
or outcomes
Knowledge Representation
•
•
•
•
Knowledge is represented in a computer in the form of rules (Production
rule).
Consists of an IF part and THEN part.
IF part lists a set of conditions in some logical combination.
If the IF part of the rule is satisfied; consequently, the THEN part can be
concluded.
Knowledge Representation
•
•
If flammable liquid was leaked then call the fire department.
If the material is acid and smells like vinegar then the leak material is
acetic acid.
Basic Concept of an Expert System Function
•
•
•
•
•
The expert system consists of two major components: knowledge base
and inference engine.
Knowledge base contains the domain knowledge which is used by the
inference engine to draw conclusions.
The inference engine is the generic control mechanism that applies the
obvious knowledge to the task-specific data to arrive at some conclusion.
When a user supplies facts or relevant information of query to the expert
system he receives advice or expertise in response.
That is given the facts it uses the inference engine which in turn uses the
knowledge base to infer the solution.
Knowledge base
•
•
•
It is expert systems contain both factual and heuristic knowledge.
Factual knowledge is that knowledge of task domain that is widely shared, typically
found in textbooks or journals.
Heuristic knowledge is less exhaustive, more experiential, more judgmental
knowledge of performance.
Reasoning
•
Two methods of reasoning when using inference rules:
(i)
Backward chaining: it starts with list of goals and works backward if there is data
which will allow it to conclude these goals.
(ii) Forward chaining: it starts with data available and then concludes a desired goal.
CHARACTERISTICS OF EXPERT SYSTEMS
•
•
•
•
High performance: They should perform at the level of a human expert.
Adequate response time: They should have the ability to respond in a
reasonable amount of time. Time is crucial especially for real time systems.
Reliability: They must be reliable and should not crash.
Understandable: They should not be a black box instead it should be able
explain the steps of the reasoning process. It should justify its conclusions
in the same way a human expert explains why he arrived at particular
conclusion.
1. Dendral
2. Mycin
Some Prominent Expert Systems
3. PXDES
4. R1/Xcon
Two early expert systems broke ground in the healthcare space for
medical diagnoses: Dendral, which helped chemists identify organic
molecules, and MYCIN, which helped to identify bacteria to
recommend antibiotics and dosages.
Expert system-MYCIN
• An early expert system developed in early 1970s at Stanford University
• <<Rule-Based Expert Systems:
The MYCIN Experiments of the Stanford Heuristic Programming Project >>
• This expert system was designed to identify bacteria causing severe
infections
REASONING & PROBLEM SOLVING STRATEGY
• MYCIN could use backward chaining to find out whether a possible
bacteria was to blame.
• “Certainty factor” is used for an assessment of the likelihood of one
bacteria.
• MYCIN’s problem solving strategy was simple:
• For each possible bacteria: Using backward chaining, try to prove that it is the
case, finding the certainty.
• Find a treatment which ” covers” all the bacteria above some level of certainty.
Problem
Solving
• When trying to proveMYCIN:
a goal through
backward chaining,
system
•
•
could ask user certain questions.
Certain facts are marked as “askable”, so if they couldn’t be proved,
ask the user.
The ask procedure is carried out in following style of dialogue:
• MYCIN: Has the patient had neurosurgery?
• USER: No.
• MYCIN: IS the patient a burn patient?
• USER: No.
•…
• MYCIN: It could be Diplococcus..
Modeling Human Diagnostic Strategies
• Problem Solving Strategy used in MYCIN only works when small number of
hypotheses (e.g., bacteria).
• For hundreds of possible diseases, need a better strategy.
• Later medical diagnostic systems used an approach based on human expert
reasoning.
EXAMPLES OF ES IN MEDICAL -PXDES
• It is example of medical expert system.
• It is a lung disease, X-ray diagnosis.
• It takes our lungs picture from upper side of body which looks like a
shadow.
• The shadow is used to determine the type and degree of harmness.
• These systems include three modes:
1.The knowledge base
2.The explanation interface
3.The knowledge acquisition
Contd..
(1) KNOWLEDGE BASE:-
• It contains the data of X-ray representations of various
stages of the disease.
(2) EXPLANATION INTERFACE:-
• It details the conclusion.
(3) KNOWLEDGE ACQUISITION:-
• It allow medical experts to add or change information in the
system.
Medical Expert Systems Today
• Medical expert systems were quite effective in evaluations
comparing their performance with human experts.
• Support the physicians decisions, rather than doing the whole
diagnosis.
• Include many useful support materials, such as report generating
tools, reference material etc.
• Availability: Expert systems are available easily due to mass
production software.
OF EXPERT
SYSTEMS
• Cheaper:ADVANTAGES
The cost of providing expertise
is not expensive.
• Reduced danger: They can be used in any risky environments
where humans cannot work with.
• Permanence: The knowledge will last long indefinitely.
• Multiple expertise: It can be designed to have knowledge of many
experts.
• Explanation: They are capable of explaining in detail the reasoning
that led to a conclusion.
• Fast response: They can respond at great speed due to the
inherent advantages of computers over humans.
• Unemotional and response at all times: Unlike humans, they do
not get tense, fatigue or panic and work steadily during emergency
Expert System Technology
There are several levels of ES technologies available.
Two important things to keep in mind when selecting ES tools include:
1. The tool selected for the project has to match the capability and sophistication of
the projected ES, in particular, the need to integrate it with other subsystems
such as databases and other components of a larger information system.
2. The tool also has to match the qualifications of the project team.
Expert System Technology
Expert systems technologies include:
1. Specific expert systems
- These expert systems actually provide recommendations in a specific
task domain.
2. Expert system shells
- Collection of software packages & tools to design, develop,
implement, and maintain expert systems.
- are the most common vehicle for the development of specific ESs. A shell
is an expert system without a knowledge base.
- A shell furnishes the ES developer with the inference engine, user
interface, and the explanation and knowledge acquisition facilities.
Expert System Technology
- Expert system shells are the software containing an interface, an
inference engine, and the formatted skeleton of a knowledge base. In
essence, an expert system shell is an empty bowl to be filled with the
expert knowledge elements that the inference engine may process for
users.
- Domain-specific shells are actually incomplete specific expert
systems, which require much less effort in order to field an actual
system.
Contd..
3. Expert system development environments
- these systems expand the capabilities of shells in various directions. They run on
engineering workstations, minicomputers, or mainframes; offer tight integration with
large databases; and support the building of large expert systems.
4. High-level programming languages
Several ES development environments have been rewritten from LISP into a
procedural language more commonly found in the commercial environment, such as
C or C++. ESs are now rarely developed in a programming language.