Knowledge Based System in Artificial Intelligence and Machine Learning.

1. DISCOVER . LEARN . EMPOWER
UNIVERSITY INSTITUTE OF
ENGINEERING
DEPARTMENT OF COMPUTER SCIENCE
AND ENGG.
Bachelor of Engineering (Computer Science & Engineering)
Artificial Intelligence and Machine Learning(CST-303/ITT-
303)
Prepared by:
Jasneet Kaur(E7747)
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Chapter-2.1 Lecture-16
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2. AI
Creating Machine
with
Intelligence
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 Knowledge Based System

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5. KNOWLEDGE BASED SYSTEM
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 A knowledge-based system (KBS) is a computer program that reasons and uses
a knowledge base to solve complex problems. The term is broad and refers to many
different kinds of systems. The one common theme that unites all knowledge based
systems is an attempt to represent knowledge explicitly and a reasoning system that
allows it to derive new knowledge. Thus, a knowledge-based system has two
distinguishing features: a knowledge base and an inference engine.
 The first part, the knowledge base, represents facts about the world, often in some form
of subsumption ontology (rather than implicitly embedded in procedural code, in the way a
conventional computer program does). Other common approaches in addition to a
subsumption ontology include frames, conceptual graphs, and logical assertions.
 The second part, the inference engine, allows new knowledge to be inferred. Most
commonly, it can take the form of IF-THEN rules coupled with forward or backward
chaining approaches. Other approaches include the use of automated theorem provers,
logic programming, blackboard systems, and term rewriting systems such as CHR
(Constraint Handling Rules).

6. The first knowledge-based systems were rule based expert systems. One of the most
famous was Mycin, a program for medical diagnosis. These early expert systems
represented facts about the world as simple assertions in a flat database, and used rules
to reason about (and as a result add to) these assertions. Representing knowledge
explicitly via rules had several advantages:
 Acquisition and maintenance. Using rules meant that domain experts could often define
and maintain the rules themselves rather than via a programmer.
 Explanation. Representing knowledge explicitly allowed systems to reason about how they
came to a conclusion and use this information to explain results to users. For example, to
follow the chain of inferences that led to a diagnosis and use these facts to explain the
diagnosis.
 Reasoning. Decoupling the knowledge from the processing of that knowledge enabled
general purpose inference engines to be developed. These systems could develop
conclusions that followed from a data set that the initial developers may not have even
been aware of.[3]

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8.  Knowledge Acquisition is the transformation of knowledge from the forms in which it exists
into forms that can be used in a knowledge based system
 The primary goal discover, develop and implement efficient, effective method of
knowledge acquisition
 It is a process of adding new knowledge to a knowledge base refining, improving
previously acquire knowledge
 Acquisition is usually associated with some definite purpose such as expanding the
capabilities of system, improving or enhancing the performance of some specific tasks
 Acquired knowledge consist of facts, rules, concept, procedure, formulas, relationship,
stats, plans, heuristic or any relevant information
 The sources can be anyone of the following like-that , report, electronic document,
database, newspaper, news channel, soft copy of document etc

9.  Handcrafting - means code knowledge is converted into program directly
 Knowledge Engineering - means working with expert system to organise knowledge in
a suitable form for an expert system to use.
 Machine Learning - means to extract the knowledge from training examples
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 most knowledge is in the heads of experts
 experts have vast amount of knowledge
 experts are very busy and valuable people
 each expert doesn't know everything
 knowledge is short lived
 difference in expert opinions

11. THANK YOU
For queries
Email: jasneete7747@cumail.in
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