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Artificial Intelligence

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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Artificial Intelligence

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  1. Artificial Intelligence

  2. Contents • History of A.I • Knowledge Representation System

  3. 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)

  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. 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. 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 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

  15. 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?)

  16. 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?)

  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 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.

  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 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

  22. 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

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

  24. Let’s see how a Robo girldefines 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 priority90

  28. Agent 2 Will you dance with me? Robo girl has given the priority10 !!

  29. Result is …

  30. 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!’

  31. 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’

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

  33. SO…..

  34. Robot is bowing……..

  35. Method 3

  36. Let’s see a photograph of Mr. Bobtaken Last Year.

  37. Date of photographApril 24, 2006

  38. Now see a photograph of This Year.

  39. Date of photographApril 24, 2007

  40. 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’

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

  42. ANSWER IS NO

  43. THANK YOU

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