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 History of A.I  Knowledge Representation System.

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Presentation on theme: " History of A.I  Knowledge Representation System."— Presentation transcript:

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2  History of A.I  Knowledge Representation System

3  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 Brief History AI (1 of 7)

4  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)

5  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)

6  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)

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. Brief History AI (5 of 7)

8  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)

9  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)

10  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)

11  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)

12 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 THEN 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)

13  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)

14 Object Value Operator 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)

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

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

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

18 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., IFseason is Autumn ANDsky is cloudy ANDwind 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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20  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 Rule 1 Rule 2 IF THEN 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 The Agent is a ManThe Agent is a Woman

22 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  How can you define the Highest Priority?

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

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

26 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? Robo girl has given the priority 90

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

29  Result is …

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31 Rule 1 Rule 2 IF (Priority 90)IF (Priority 10) THEN 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 Say ‘Yes’ and Dance Say ‘Sorry Sir!’

32 Rule 1Rule 2 IF THEN The Agent is in the front AND The Agent is a man The Agent is in the front AND The Agent is a man AND The Agent is bowing Say ‘Hello’ Bow

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

34 SO …..

35 Robot is bowing……..

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38 Date of photograph April 24, 2006

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41 Rule 1 Rule 2 IF THEN The Question is “Is Mr. Bob bald?” [08:16PM 4/24/2006] Say ‘Yes’Say ‘NO’ The Question is “Is Mr. Bob bald?” [08:16PM 4/24/2007]

42 Is Mr. Bob bald?

43 ANSWER IS NO

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