Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning: STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning, A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation, Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead Strategies.

1. Topic To Be Covered:
Rule base expert system
Jagdamba Education Society's
SND College of Engineering & Research Centre
Department of Computer Engineering
SUBJECT: Artificial Intelligence & Robotics
Lecture No-02(Unit-02)
Prof.Dhakane Vikas N

2. Rule base expert system
Definitions of Rule Base Expert System
Rule base system or Rule base Expert System or Expert System is a piece
of software which uses database of expert knowledge to offer advice or
make decisions in such areas as medical diagnosis, account, coding , games
etc.
Rule base system or Rule base Expert System or Expert System are the
computer applications developed to solve complex problems in a particular
domain, at the level of extra-ordinary human intelligence and expertise.

3. Rule base expert system
Definitions of Rule Base Expert System
A rule-based system is a system that applies human-made rules to store,
sort and manipulate data. In doing so, it mimics human intelligence.
To work, rule-based systems require a set of facts or source of data, and a
set of rules for manipulating that data.
Rule-based systems, unsurprisingly, work based on rules.
For example, a trigger might be an email containing the word “facebook”.
An action might then be to forward the email to the Social Tab.

4. Rule base expert system
Characteristics of Rule Base Expert System
High performance
Understandable
Reliable
Highly responsive

5. Rule base expert system
Capabilities of Rule Base Expert System
Advising
Instructing and assisting human in decision making
Deriving a solution
Diagnosing
Explaining
Interpreting input
Predicting results
Justifying the conclusion

6. Rule base expert system
Components of Rule Base Expert System
The components of Expert System include −
Knowledge Base
Inference Engine
User Interface

7. Rule base expert system

8. Rule base expert system
Expert System Cell In Rule Base Expert System
Knowledge Acquisition Subsystem
This is a subsystem that helps experts to build knowledge bases. It collect
knowledge from various expert(expert must be domain related ) and
represent it into Knowledge Base using appropriate Knowledge
Representation Language.
The process of collecting the relevant domain knowledge needed to solve
problems and building (coding) the knowledge base (knowledge
engineering) continues to present the biggest bottleneck in building expert
systems.

9. Rule base expert system
Components of Rule Base Expert System
I. Knowledge Base
It contains domain-specific and high-quality knowledge. Knowledge is
required to exhibit intelligence. The success of any ES majorly depends upon
the collection of highly accurate and precise knowledge.
What is Knowledge?
The data is collection of facts. The information is organized as data and facts
about the task domain. Data, information, and past experience combined
together are termed as knowledge.

10. Rule base expert system
Components of Rule Base Expert System
I. Knowledge Base
Components of Knowledge Base
A]Factual Knowledge(Static Knowledge)
It is the information widely accepted by the Knowledge Engineers and
scholars in the task domain.
B]Heuristic Knowledge(Dynamic Knowledge)
It is about practice, accurate judgment, one’s ability of evaluation, and
guessing.

11. Rule base expert system
Components of Rule Base Expert System
Knowledge representation
It is the method used to organize and formalize the knowledge in the
knowledge base. It is in the form of IF-THEN-ELSE rules.
Knowledge Acquisition
The success of any expert system majorly depends on the quality,
completeness, and accuracy of the information stored in the knowledge base.
The knowledge base is formed by readings from various experts, scholars,
and the Knowledge Engineers.

12. Rule base expert system
Components of Rule Base Expert System
The knowledge engineer is a person with the qualities of empathy,
quick learning, and case analyzing skills.
He acquires information from subject expert by recording,
interviewing, and observing him at work, etc.
He then categorizes and organizes the information in a meaningful
way, in the form of IF-THEN-ELSE rules, to be used by interference
machine.
The knowledge engineer also monitors the development of the ES.

13. Rule base expert system
Components of Rule Base Expert System
II. Inference Engine
 This refers to the Inference mechanisms that are used for manipulating
the symbolic information and knowledge contained in the knowledge base
in order to form a reasoning path towards the solution of a problem.
 In the field of artificial intelligence, inference engine is a component of the
system that applies logical rules to the knowledge base to deduce new
information.

14. Rule base expert system: Components of es
To recommend a solution, the Inference Engine uses the following
strategies −
A. Forward Chaining
It is a strategy of an expert system to answer the question, “What can
happen next?”
Here, the Inference Engine follows the chain of conditions and derivations
and finally deduces the outcome. It considers all the facts and rules, and sorts
them before concluding to a solution.
This strategy is followed for working on conclusion, result, or effect. For
example, prediction of share market status as an effect of changes in interest
rates.

15. Rule base expert system: Components of es
To recommend a solution, the Inference Engine uses the following
strategies −
B. Backward Chaining
With this strategy, an expert system finds out the answer to the question,
“Why this happened?”
On the basis of what has already happened, the Inference Engine tries to
find out which conditions could have happened in the past for this result. This
strategy is followed for finding out cause or reason.
For example, diagnosis of blood cancer in humans.

16. Rule base expert system
Components of Rule Base Expert System
III. Explanation Subsystem
This Explanation Subsystem explain the solution or result to the
user(non-expert). It explain the result or solution into “Why” and “How”.
Explanation Subsystem try to give reasons why a certain cause of action or
a certain conclusion was chosen and how this solution is chosen by an Expert
System.

17. Rule base expert system
Components of Rule Base Expert System
IV. User Interface
This represents the means of communication with the user.
The users enters a request in Natural Language, and the Expert System
processes the Request to provide a result by using an Inference rule(s) to
select the appropriate solution from a Knowledge Base and user interface
represent that solution in Natural Language.