This document discusses the constituents of soft computing and how they relate to conventional artificial intelligence. Soft computing includes neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods like genetic algorithms. These methods form the core of soft computing and complement conventional AI, which relies on symbolic manipulation and knowledge-based systems. While knowledge representation has limited conventional AI, soft computing and artificial intelligence share the goal of building and understanding machine intelligence through biologically inspired approaches.

1. D R . P R A F U L L C H A N D R A N A R O O K A
D E P A R T M E N T O F C O M P U T E R S C I E N C E
S C H O O L O F E N G I N E E R I N G & S Y S T E M S
S C I E N C E S
M D S U N I V E R S I T Y , A J M E R
SC Constituents and Conventional AI
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2. SC Constituents and Conventional AI
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 “SC is an emerging approach to computing which parallel
the remarkable ability of the human mind to reason and
learn in a environment of uncertainty and imprecision”
[Lotfi A. Zadeh, 1992]
 SC consists of several computing paradigms including:
 NN
 Fuzzy set theory
 Approximate reasoning
 Derivative-free optimization methods such as genetic algorithms
(GA) & simulated annealing (SA)

3. SC constituents (the first three items) and
conventional AI
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4. Continue…….
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 These methodologies form the core of SC
 In general, SC does not perform much symbolic
manipulation
 SC in this sense complements conventional AI
approaches

5. character recognizer
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6. Features of Conventional AI
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 From conventional AI to computational
intelligence
 Conventional AI manipulates symbols on the assumption
that human intelligence behavior can be stored in
symbolically structured knowledge bases: this is known
as: “ The physical symbol system hypothesis
 The knowledge-based system (or expert system) is an
example of the most successful conventional AI product

7. What is an expert system?
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An expert system is software that
uses a knowledge base of human
expertise for problem solving, or to
clarify uncertainties where
normally one or more
human experts would need to be
consulted

8. Expert system
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9. Building blocks of expert system
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 Knowledge base: factual knowledge and heuristic
knowledge
 Knowledge representation: in the form of rules
 Problem solving model: forward chaining or backward
chaining
 Knowledge base: knowledge gained by an individual user
Note:-
 Knowledge engineering:- building an expert system
 Knowledge engineers:- practitioners.

10. Applications of expert system
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1. Diagnosis and Troubleshooting of Devices and
Systems of All Kinds
2. Planning and Scheduling
3. Configuration of Manufactured Objects from
Subassemblies
4. Financial Decision Making
5. Knowledge Publishing
6. Design and Manufacturing

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12. Several definitions have been given to
conventional AI
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 “AI is the study of agents that exists in an
environment and perceive and act” [S. Russel & P.
Norvig]
 “AI is the act of making computers do smart things”
[Waldrop]
 “AI is a programming style, where programs operate
on data according to rules in order to accomplish
goals” [W.A. Taylor]

13. Continue…….
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 Knowledge acquisition and representation has
limited the application of AI theories (shortcoming
of symbolisms)
 SC has become a part of “modern AI”
 Researchers have directed their attention toward
biologically inspired methodologies such as brain
modeling, evolutionary algorithm and immune
modeling

14. Continue…….
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 These new paradigms simulate chemico-biological
mechanisms responsible for natural intelligence
generation
 SC and AI share the same long-term goal: build
and understand machine intelligence
 An intelligent system can for example sense its
environment (perceive) and act on its perception
(react)
 SC is evolving under AI influences that sprang
from cybernetics (the study of information and
control in human and machines)