1. Artificial Intelligence

2. Contents
History of A.I
Knowledge Representation System

3. Brief History AI (1 of 7)
The Dark Ages [ The birth of A.I.]: Duration:
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,
The Dartmouth College summer workshop on machine intelligence, Artificial neural networks and automata theory, 1956

4. Brief History AI (2 of 7) Contributions:
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

5. Brief History AI (3 of 7) Duration: late1960s-early 1970s
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

6. Brief History AI (4 of 7) Duration: 1970s – mid 1980s
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.

7. Brief History AI (5 of 7)
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.

8. Brief History AI (6 of 7) Duration: early 1970s – onward
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

9. Brief History AI (7 of 7) Contributions:
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.)

10. Knowledge Representation System (1 of 6)
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.

11. Knowledge Representation system (2 of 6)
(Rule-Based)
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.

12. 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>

13. Knowledge Representation system (4 of 6)
(Rule-Based)
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.

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

15. Knowledge Representation system (6 of 6) (What rules can represent?)
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’

16. THEN the action ‘check the fuel tank’
Knowledge Representation system
(What rules can represent?)
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’

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

18. Advantages and Disadvantages of Rule-based Knowledge Representation
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.

19. Conflict Resolutions in Rule-Based Systems

20. 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.

21. Example of Conflict The Agent is a Man The Agent is a Woman Rule 1IF 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 Agent is a Man
The Agent is a Woman

22. How to Resolve A Conflict?
According to Yoshiaki Shirai and 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

23. Fire the rule with the Highest priority
How can you define the Highest Priority?

24. Let’s see how a Robo girl defines priority

25. A Robo Girl
It’s just an example. I’ll say later about this robot.

26. 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.

27. Will you dance with me?
Agent 1
Robo girl has given the priority 90

28. Will you dance with me?
Agent 2
Robo girl has given the priority 10 !!

29. Result is …

31. Method 1 Highest Priority
Rule 1
Rule 2
IF (Priority 90)
IF (Priority 10)
The Agent asks to dance
AND The Agent’s age is below 35
The Agent asks to dance
AND The Agent’s age is above 60
THEN
THEN
Say ‘Yes’ and Dance
Say ‘Sorry Sir!’

32. Method 2: Longest Match Bow Say ‘Hello’ 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’

33. 1. Agent is in the front
2. Agent is a man
3. Agent is bowing

34. SO…..

35. Robot is bowing……..

36. Method 3

37. Let’s see a photograph of Mr. Bob taken Last Year.

38. Date of photograph April 24, 2006

39. Now see a photograph of This Year.

40. Date of photograph April 24, 2007

41. Method 3 Timestamp The Question is “Is Mr. Bob bald?”
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’

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

43. ANSWER IS NO

44. THANK YOU