Artificial Intelligence Lecture Slide 02

Artificial Intelligence

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Artificial Intelligence
Lecture #2

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Contents
Summary of Previous Lecture
History of A.I
Knowledge Representation System

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Summary of Previous Lecture
Knowledge?
Intelligence?
Intelligent Machine?
What is A.I.?

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The Dark Ages[ The birth of A.I.]: Duration: 1943-56
Contributions: First work by Warren McCulloch & Walter
Pitts [ 1943 ]. It was on the central nervous system-a model
of neurons of the brain.
Turing, Computing Machinery & intelligence, 1950
ENIAC (Electronic Numerical Integrator And Calculator)
by Von Neumann.
Shannon, Programming a computer for playing chess,
1950.
The Dartmouth College summer workshop on machine in
telligence, Artificial neural networks and automata theory
, 1956
Brief History AI (1 of 7)

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Rises of A.I: Duration: 1956-late 1960s
Contributions:
John McCarthy (inventor of the term Artificial Intelligence)
defined the high level language LISP –one of the oldest pro
gramming language, which is still in current use.
General Problem Solver (GPS) by Newell & Simon, 1960
Human Problem Solving ideas by Newell & Simon, 1972
A framework for representing knowledge by Minsky, 1975
Brief History AI (2 of 7)

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The Era of unfulfilled promises [ The impact of reality]
Duration: late1960s-early 1970s
Contributions:
The Complexityof theorem proving procedures by
Cook, 1971
Reducibility Among Combinatorial Problems by Kar
p 1972
Brief History AI (3 of 7)

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The Discovery of expert systems
Duration: 1970s –mid 1980s
Contributions:
DENDRAL –the first successful knowledge-based system by Fei
genbum, Bachanan & Lederberg.
MYCIN –another expert system by Feigenbum and Shortllife
PROSPECTOR –an expert system for mineral exploration devel
oped by Stanford Research Institute
PROLOG –A logic programming language by Colmerauer, Rous
sel & Kowalski
EMYCIN –Empty MYCIN, a domain-independent version of M
YCIN, developed by Stanford University.
Brief History AI (4 of 7)

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The Rebirth of Artificial Neural Networks:1965 –onward
Contributions:
Neural Networks & Physical Systems with Emergent
Collective Computational Abilities by Hopfield.
Self-Organized Formation of Topological Correct Feat
ure maps by Kohonen
Parallel Distributed Processing, by Rumelhart & McCl
elland
The First IEEE International Conference on Neural Ne
tworks.
Brief History AI (5 of 7)

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Evolutionary computation [Learning by doing]
Duration: early 1970s –onward
Contributions:
Adaptation in Natural and Artificial Systems, by Holla
nd
Genetic Programming: On the Programming of the com
puters by means of Natural Selection by Koza
Evolutionary computation –Towards a new philosophy
of machine intelligence by Fogel
Brief History AI (6 of 7)

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Computing with Words: late 1980s –onwards
Contributions:
Fuzzy sets & Algorithms by Zadeh
Application of Fuzzy logic to Approximate Reasonin
g using Linguistic Synthesis by Mamdani
Expert Systems and Fuzzy Systems, by Negoita.
The First IEEE International Conference on Fuzzy Sy
stems
Neural Networks and Fuzzy Systems by Kosko
Fuzzy Logic, MATLAB Application Toolbox by the
MathWork, Inc.)
Brief History AI (7 of 7)

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Primary objective of A.I:
To store knowledge so that programs can process
it and achieve the resemblance of human intellige
nce.
Knowledge Representation techniques
•Rule-based
•Frame-based
•Semantic Network, etc.
Knowledge Representation System (1 of 6)

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Features:
This is the most popular choice for building kno
wledge-based systems.
Rule is the most commonly used type of knowle
dge representation, which can be defined as an
IF-THEN structure.
Knowledge Representation system (2 of 6)
(Rule-Based)

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THEN PART
•It is called
consequent or
conclusion or action.
IF Part
•It is called
antecedent or
premise or
condition.
Rules
So, the basic construct is-
IF <antecedent>
THEN <consequent>
An Example of this construct
RULE #1
IF the ‘traffic light’ is green
THEN the action is go
RULE #2
IF the ‘traffic light’ is red
THEN the action is stop
Knowledge Representation system (3 of 6)
(Rule-Based)

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A rule can have multiple antecedents joined by the
keywords AND, OR or a combination of both.
For example,
RULE#3
IF ‘age of the customer’ < 18
AND ‘cash withdrawal’ > 1000
THEN ‘signature of the parent’ is required.
Knowledge Representation system (4 of 6)
(Rule-Based)

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Object ValueOperator
Each antecedent & consequent has 3 components
RULE#3
IF ‘taxable income’ > 25000
THEN ‘Medicare levy’ = ‘taxable income’ * 1.5 / 100
Knowledge Representation system (5 of 6)
(Rule-Based)

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•Relation
IF the ‘fuel tank’ is empty
THEN the ‘car’ is dead
•Recommendation
IF the ‘season’ is autumn
AND the ‘sky’ is cloudy
THEN the ‘advice’ is ‘take an umbrella’
•Directive
IF the ‘car’ is dead
AND the ‘fuel tank’ is empty
THEN the action is ‘refuel the car’
Knowledge Representation system (6 of 6)
(What rules can represent?)

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•Strategy
IF the ‘car’ is dead
THEN the action ‘check the fuel tank’
step 1 is complete
IF step 1 is complete
AND the ‘fuel tank’ is full
THEN the action is ‘check the battery’
step 2 is complete.
•Heuristic
IF the sample is liquid
AND the ‘sample pH’ < 6
AND the ‘sample smell’ is vinegar
THEN the ‘sample material’ is ‘acetic acid’
Knowledge Representation system
(What rules can represent?)

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Knowledge-Base
Rule: IF-THEN
Database
FACT
Inference engine
Explanation Facilities
User Interface
User
Basic structure of rule-based expert system
Knowledge Representation system

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Disadvantage
•Opaque relations between rules
•Ineffective search strategy
•Inability to learn.
Advantages
Natural Knowledge representation
Uniform structure
Separation of knowledge from the
inference engine
Dealing with incomplete and uncertain
knowledge. E.g.,
IF season is Autumn
AND sky is cloudy
AND wind is low
THEN forecast is clear {cf 0.1};
forecast is rain {cf 0.9}
Rule-base
Expert system
Advantages and Disadvantages of
Rule-based Knowledge Representation

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Recommended Textbooks
[Negnevitsky,2001]M.Negnevitsky“ArtificialIntelligence:Aguideto
IntelligentSystems”,PearsonEducationLimited,England,2002.
[Russel,2003]S.RussellandP.NorvigArtificialIntelligence:AModern
ApproachPrenticeHall,2003,SecondEdition
[Patterson,1990]D.W.Patterson,“IntroductiontoArtificialIntelligence
andExpertSystems”,Prentice-HallInc.,EnglewoodCliffs,N.J,USA,19
90.
[Minsky,1974]M.Minsky“AFrameworkforRepresentingKnowledge”,
MIT-AILaboratoryMemo306,1974.
[Hubel,1995]DavidH.Hubel,“Eye,Brain,andVision”
[Horn,1986]B.K.P.Horn,RobotVision,MITPress,1986.
[Ballard,1982]D.H.BallardandC.M.Brown,“ComputerVision”,
PrenticeHall,1982.

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End of Presentation
Questions/Suggestions
Thanks to all !!!