An expert system is a computer program that mimics human decision-making to solve complex problems, utilizing a knowledge base and an inference engine. They are domain-specific and consist of components such as working memory, explanation subsystem, and user interface. The document discusses various elements of expert systems, their architecture, building process, types, and notable existing systems like Mycin and Dendral.

1. Expert Systems
• An expert system
• is a computer program that is designed to solve complex problems and to provide
decision-making ability like a human expert.
• It acquires relevant knowledge from its knowledge base, and interprets it as per the
user’s problem.
• It uses both facts and heuristics like a human expert.
• These systems are designed for a specific domain, such as medicine, science, etc.

2. • The architecture of Expert system consists 5 components
1. Knowledge Base (KB)
repository of rules that direct the use of knowledge, facts. It contains the knowledge
necessary for understanding, formulating, & problem solving.
2. Working Memory
• it records and stores the current problem & record intermediate results
• records Intermediate Hypothesis & Decisions
1. Plan
2. Agenda
3. Solution
Knowledge Base
Knowledge facts

3. 3. Inference Engine
• used to infer results from user input & Knowledge Base. It is the brain of the Expert
System, the control structure(rule interpreter) and it provides methodology for
reasoning
4. Explanation Subsystem
• Traces responsibility & explains the Expert System behaviour by interactively
answering question:
Why?, How?, What?, Where?, When?, Who?
5. User Interface
• interfaces with user through Natural Language Processing (NLP), or menus &
graphics.
• Acts as Language Processor for friendly, problem-oriented communication
• Shell = Inference Engine + User Interface

5. • Expert System building process
• There are 3 basic parameters for expert system building process:
• Domain Exploration
• Exploration of the knowledge about the domain in which the system is operational
• Expertise Transfer
Expertise
Transfer
Representation
Acquisition
Outcomes
Inference Building

6. • Meta Knowledge
• is a knowledge about available knowledge
• it is systematic as well as domain independent knowledge
• helps in building an expert system that is less dependent on a particular domain
• Meta Knowledge structure is given as
• Confidence Factors
• Are the values associated with a particular variable
• They indicate the degree of truth for the particular variable
• Based on the confidence factor, the expert system can give guidelines or recommendations
• Confidence factors can be made available in various ways
• User input
• Rule set
Data Information Knowledge Meta-Knowledge

7. • User input
• A user specifies the confidence level for some specific variable by direct or indirect
means
• direct
• Asking a student how good you are in a particular subject in a 1 to 10 scale
• The possible answer is 8 then the confidence factor is 80%
• indirect form
• A database is used by ES to obtain the confidence factor
• A user recommendation stored in the form of a table
• Rule set
• by the use of rules
• By the rule of
• ‘if(Degree = CS) and (Masters = CS) then High-Salary=True’ with a confidence
factor of 75
• These rules are based on previous available information

8. • Probabilistic approaches can assist in developing these confidence factors based on the
previous available probable values for any variable
• A simple probable value can be treated as confidence factor
Existing Expert Systems
• DENDRAL:
• It was an artificial intelligence project that was made as a chemical analysis expert
system.
• It was used in organic chemistry to detect unknown organic molecules with the help of
their mass spectra and knowledge base of chemistry.
• PXDES:
• It is an expert system that is used to determine the type and level of lung cancer. To determine the
disease, it takes a picture from the upper body, which looks like the shadow. This shadow
identifies the type and degree of harm.
• CaDeT:
• The CaDet expert system is a diagnostic support system that can detect cancer at early stages.

9. Mycin
• earliest backward chaining expert systems
• designed to find the bacteria causing infections like bacteraemia and meningitis.
• also used for the recommendation of antibiotics and the diagnosis of blood clotting diseases.
• Building of MYCIN Expert System
• Pathology Experts provide information about the causes, symptoms, and other
knowledge in that domain.
• The Knowledge Base of the MYCIN is updated.
• To test MYCIN, the doctor provides a new problem to it.
• The problem is to identify the presence of the bacteria by giving the details of a patient,
including the symptoms, current condition, and medical history.
• The Expert System will need a questionnaire to be filled by the patient to know the
general information about the patient, such as gender, age, etc.

10. • System collects all the information, so it will find the solution for the problem by applying
if-then rules using the inference engine and using the facts stored within the KB.
• In the end, it will provide a response to the patient by using the user interface.
Expert System Shells
• The early expert systems were built using LISP (LISt Processing) language.
• To facilitate building of ES, a platform which would contain most of the common
procedures that an ES would require.
• Shells were developed, called as AI tools or Skeletons.
• An expert system shell is a software which contains user interface, a format for declarative
knowledge in the knowledge base and an inference engine.
• Shell = Rules + Interpreter
• Interpreter can be separated from the domain specific knowledge to build new systems

11. • Example
• EMYCIN (empty MYCIN ) was derived from MYCIN.
• Knowledge engineer will use these shells to develop an ES
• Shell structure
Inference Knowledge Explanations
Mechanism base-subset
User interface
Domain/Application specific data
and
Knowledge base
Shell

12. Explanations
• An important feature of ES, shows the ability of the system to express itself
• For any action there is a necessity to have explanation, as we do not accept things unless
they are clarified and we are convinced about them
• For making the ES effective, it should be interactive
• The explanations are based on the rules that are used for the inference process

13. Knowledge acquisition
• A process which allows the experts to enter their knowledge or expertise into the expert
system, and to refine it later as and when required.
• The knowledge acquisition process can usually comprise of three principal stages:
• Knowledge elicitation is the interaction between the expert and the knowledge
engineer/program to elicit the expert knowledge in some systematic way.
• The knowledge thus obtained is usually stored in some form of human friendly intermediate
representation.
• The intermediate representation of the knowledge is then compiled into an executable
form (e.g. production rules) that the inference engine can process
Expert System
Knowledge
feedback

14. Self Explaining System
• is a feature of ES that shows the ability to explain itself
• It is able to justify why a particular question is being asked and how it has arrived to the
conclusion.
Rule based expert systems
• It consists of rules(KB), set of facts and an interpreter
• Rule based ES are designed so that they can have heuristic reasoning and proper explanations
User Interface Explanations
Inference
Mechanism
KB
Comprising
rules
Facts

15. • Instead of representing knowledge in a relatively declarative or static way (as a bunch of
things that are true),
• rule-based system represent knowledge in terms of a bunch of rules that tell you what
you should do or what you could conclude in different situations.
• A rule-based system consists of a bunch of IF-THEN rules, a bunch of facts, and some
interpreter controlling the application of the rules, given the facts.
• The inference mechanism referred as Inference Engine, seeks the necessary information
and the relationships from the KB to answer

16. • Inference
• In a forward chaining system
• start with the initial facts, and keep using the rules to draw new conclusions (or take certain actions) given those
facts.
• Inference rules are successively applied to elements of the knowledge base until the goal is
reached
Inference mechanisms
Forward chaining
knowledge base and set of inference rules.
deriving a particular goal
matching facts to the antecedents

17. • Backward chaining
• In a backward chaining system
• start with some hypothesis (or goal) trying to prove, and
• look for rules that would conclude that hypothesis or setting new sub goals to prove
Inference mechanisms
Backward chaining
knowledge base and set of inference rules.
antecedent of the relation is added to the list of
goals
matching the goal of the search to the consequents
of the relations stored
Search proceeds until a goal be matched against a fact

18. • condition  action
• premise  idea or theory with a conclusion

19. Example
• Knowledge Base given as:
• If [X croaks and eats flies] Then [X is a frog]
• If [X chirps and sings] Then [X is a bird]
• If [X is a frog] Then [X is colored green]
• If [X is a bird] Then [X is colored yellow]
• [Fritz croaks and eats flies]
• Goal:
• [Fritz is colored Y]?

20. • Workflow
If [X croaks and eats flies]
Then [X is a frog]
[Fritz croaks and eats flies]
[Fritz is a frog]
Knowledge Base
If [X croaks and eats flies]
Then [X is a frog]
If [X chirps and sings]
Then [X is a bird]
If [X is a frog]
Then [X is colored green]
If [X is a bird]
Then [X is colored yellow]
[Fritz croaks and eats flies]
Goal
[Fritz is colored Y]?

21. • Frame-Based Expert Systems
• A frame provides a means of organising knowledge in slots to describe various attributes and
characteristics of the object
set of attributes
Car
Make
Type
Colour
has a value attached
Frame
data structure with
typical knowledge
particular object or
concept
Computer
Model
Processor
Memory
has a value attached
Price