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Artificial Intelligence. Contents. History of A.I Knowledge Representation System. 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.

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contents
Contents
  • History of A.I
  • Knowledge Representation System
slide3
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 intelligence, Artificial neural networks and automata theory, 1956

Brief History AI (1 of 7)

slide4
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 programming 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)

slide5
The Era of unfulfilled promises [ The impact of reality]

Duration: late1960s-early 1970s

Contributions:

The Complexity of theorem proving procedures by Cook, 1971

Reducibility Among Combinatorial Problems by Karp 1972

Brief History AI (3 of 7)

slide6
The Discovery of expert systems

Duration: 1970s – mid 1980s

Contributions:

DENDRAL – the first successful knowledge-based system by Feigenbum, Bachanan & Lederberg.

MYCIN – another expert system by Feigenbum and Shortllife

PROSPECTOR – an expert system for mineral exploration developed by Stanford Research Institute

PROLOG – A logic programming language by Colmerauer, Roussel & Kowalski

EMYCIN – Empty MYCIN, a domain-independent version of MYCIN, developed by Stanford University.

Brief History AI (4 of 7)

slide7
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 Feature maps by Kohonen

Parallel Distributed Processing, by Rumelhart & McClelland

The First IEEE International Conference on Neural Networks.

Brief History AI (5 of 7)

slide8
Evolutionary computation [Learning by doing]

Duration: early 1970s – onward

Contributions:

Adaptation in Natural and Artificial Systems, by Holland

Genetic Programming: On the Programming of the computers by means of Natural Selection by Koza

Evolutionary computation – Towards a new philosophy of machine intelligence by Fogel

Brief History AI (6 of 7)

slide9
Computing with Words: late 1980s – onwards

Contributions:

Fuzzy sets & Algorithms by Zadeh

Application of Fuzzy logic to Approximate Reasoning using Linguistic Synthesis by Mamdani

Expert Systems and Fuzzy Systems, by Negoita.

The First IEEE International Conference on Fuzzy Systems

Neural Networks and Fuzzy Systems by Kosko

Fuzzy Logic, MATLAB Application Toolbox by the MathWork, Inc.)

Brief History AI (7 of 7)

slide10
Primary objective of A.I:

To store knowledge so that programs can process it and achieve the resemblance of human intelligence.

Knowledge Representation techniques

Rule-based

Frame-based

Semantic Network, etc.

Knowledge Representation System (1 of 6)

slide11
Features:

This is the most popular choice for building knowledge-based systems.

Rule is the most commonly used type of knowledge representation, which can be defined as an IF-THEN structure.

Knowledge Representation system (2 of 6)

(Rule-Based)

slide12

Knowledge Representation system (3 of 6)

(Rule-Based)

Rules

  • IF Part
  • It is called antecedent or premise or condition.
  • THEN PART
  • It is called consequent or conclusion or action.
  • 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
  • So, the basic construct is-
  • IF <antecedent>
  • THEN <consequent>
slide13
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)

slide14

Object

Operator

Value

Knowledge Representation system (5 of 6)

(Rule-Based)

Each antecedent & consequent has 3 components

RULE#3

IF ‘taxable income’ > 25000

THEN ‘Medicare levy’ = ‘taxable income’ * 1.5 / 100

slide15
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?)

slide16
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?)

slide17

Knowledge Representation system

Basic structure of rule-based expert system

Knowledge-Base

Database

Rule: IF-THEN

FACT

Inference engine

Explanation Facilities

User Interface

User

slide18

Advantages and Disadvantages of

Rule-based Knowledge Representation

Rule-base

Expert system

  • 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}
  • Disadvantage
  • Opaque relations between rules
  • Ineffective search strategy
  • Inability to learn.
what is conflict
What is Conflict?
  • A situation when two or more actions are found for only one condition
  • Or, When two or more rules are fired at a time.
example of conflict
Example of Conflict

Rule 1

Rule 2

IF

IF

The Agent has two legs

AND The Agent has two hands

AND The Agent can sleep

The Agent has two legs

AND The Agent has two hands

AND The Agent can sleep

THEN

THEN

The Agentis a Man

The Agentis a Woman

how to resolve a conflict
How to Resolve A Conflict?

According to Yoshiaki Shiraiand Saburo Tsuji:

They are Three methods to resolve conflict in a rule- based system:

  • Fire the rule with the Highest priority
  • Fire the rule with the Longest Match
  • Fire the rule with the Data most recently entered
fire the rule with the highest priority
Fire the rule with the Highest priority
  • How can you define the Highest Priority?
a robo girl
A Robo Girl

It’s just an example. I’ll say later about this robot.

partner ballroom dance robot obdr
Partner Ballroom Dance Robot (OBDR).
  • The Kosuge-Wang Laboratory in Tohoku Univ.(Division of Mechanical Engineering, Dept. of Bioengineering and Robotics),Nomura Unison Inc., and Torowazo Inc.teamed up to develop this Partner Ballroom Dance Robot(OBDR).
  • This robot was exhibited at the Prototype Robot Exhibit at EXPO 2005 Aichi Japan.
agent 1

Will you dance with me?

Agent 1

Robo girl has given the priority90

agent 2
Agent 2

Will you dance with me?

Robo girl has given the priority10 !!

method 1 highest priority
Method 1Highest Priority

Rule 1

Rule 2

IF (Priority 90)

IF (Priority 10)

The Agent asks to dance

AND The Agent’s age is above 60

The Agent asks to dance

AND The Agent’s age is below 35

THEN

THEN

Say‘Yes’and Dance

Say‘Sorry Sir!’

method 2 longest match
Method 2:Longest Match

Rule 1

Rule 2

IF

IF

The Agent is in the front

AND The Agent is a man

AND The Agent is bowing

The Agent is in the front

AND The Agent is a man

THEN

THEN

Bow

Say ‘Hello’

slide33

1. Agent is in the front

2. Agent is a man

3. Agent is bowing

method 3 timestamp
Method 3Timestamp

Rule 1

Rule 2

IF

IF

The Question is “Is Mr. Bob bald?”

[08:16PM 4/24/2006]

The Question is “Is Mr. Bob bald?”

[08:16PM 4/24/2007]

THEN

THEN

Say ‘Yes’

Say ‘NO’

so now on 3 01 2013
So now on 3/01/2013
  • Is Mr. Bob bald?
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