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AI: Foundations, History, and State of the Art (Unit 1)

Explore the concepts and evolution of artificial intelligence, from the Turing Test to cognitive modeling and rational thinking. Discover the foundations in philosophy, mathematics, psychology, and linguistics. Dive into the history of AI and its current state of the art.

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AI: Foundations, History, and State of the Art (Unit 1)

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  1. Unit 1 Unit1

  2. Introduction • What is AI? • The foundations of AI • A brief history of AI • The state of the art • Introductory problems Unit1

  3. What is AI? • Intelligence: “ability to learn, understand and think” (Oxford dictionary) Unit1

  4. What is AI? Unit1

  5. Acting Humanly: The Turing Test • Alan Turing (1912-1954) • “Computing Machinery and Intelligence” (1950) Imitation Game Human Human Interrogator AI System Unit1

  6. Acting Humanly: The Turing Test • Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes. • Anticipated all major arguments against AI in following 50 years. • Suggested major components of AI: knowledge, reasoning, language, understanding, learning. Unit1

  7. Thinking Humanly: Cognitive Modelling • Not content to have a program correctly solving a problem. More concerned with comparing its reasoning steps to traces of human solving the same problem. • Requires testable theories of the workings of the human mind: cognitive science. Unit1

  8. Thinking Rationally: Laws of Thought • Aristotle was one of the first to attempt to codify “right thinking”, i.e., irrefutable reasoning processes. • Formal logic provides a precise notation and rules for representing and reasoning with all kinds of things in the world. • Obstacles: - Informal knowledge representation. - Computational complexity and resources. Unit1

  9. Acting Rationally • Acting so as to achieve one’s goals, given one’s beliefs. • Does not necessarily involve thinking. • Advantages: - More general than the “laws of thought” approach. - More amenable to scientific development than human- based approaches. Unit1

  10. The Foundations of AI • Philosophy (423 BC - present): - Logic, methods of reasoning. - Mind as a physical system. - Foundations of learning, language, and rationality. • Mathematics (c.800 - present): - Formal representation and proof. - Algorithms, computation, decidability, tractability. - Probability. Unit1

  11. The Foundations of AI • Psychology (1879 - present): - Adaptation. - Phenomena of perception and motor control. - Experimental techniques. • Linguistics (1957 - present): - Knowledge representation. - Grammar. Unit1

  12. A Brief History of AI • The gestation of AI (1943 - 1956): - 1943: McCulloch & Pitts: Boolean circuit model of brain. - 1950: Turing’s “Computing Machinery and Intelligence”. - 1956: McCarthy’s name “Artificial Intelligence” adopted. • Early enthusiasm, great expectations (1952 - 1969): - Early successful AI programs: Samuel’s checkers, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Theorem Prover. - Robinson’s complete algorithm for logical reasoning. Unit1

  13. A Brief History of AI • A dose of reality (1966 - 1974): - AI discovered computational complexity. - Neural network research almost disappeared after Minsky & Papert’s book in 1969. • Knowledge-based systems (1969 - 1979): - 1969: DENDRAL by Buchanan et al.. - 1976: MYCIN by Shortliffle. - 1979: PROSPECTOR by Duda et al.. Unit1

  14. A Brief History of AI • AI becomes an industry (1980 - 1988): - Expert systems industry booms. - 1981: Japan’s 10-year Fifth Generation project. • The return of NNs and novel AI (1986 - present): - Mid 80’s: Back-propagation learning algorithm reinvented. - Expert systems industry busts. - 1988: Resurgence of probability. - 1988: Novel AI (ALife, GAs, Soft Computing, …). - 1995: Agents everywhere. - 2003: Human-level AI back on the agenda. Unit1

  15. The State of the Art • Computer beats human in a chess game. • Computer-human conversation using speech recognition. • Expert system controls a spacecraft. • Robot can walk on stairs and hold a cup of water. • Language translation for webpages. • Home appliances use fuzzy logic. • ...... Unit1

  16. Introductory Problem: Tic-Tac-Toe Unit1

  17. Introductory Problem: Tic-Tac-Toe Program 1: 1. View the vector as a ternary number. Convert it to a decimal number. 2. Use the computed number as an index into Move-Table and access the vector stored there. 3. Set the new board to that vector. Unit1

  18. Introductory Problem: Tic-Tac-Toe Comments: 1. A lot of space to store the Move-Table. 2. A lot of work to specify all the entries in the Move-Table. 3. Difficult to extend. Unit1

  19. Introductory Problem: Tic-Tac-Toe Unit1

  20. Introductory Problem: Tic-Tac-Toe Program 2: Turn = 1 Go(1) Turn = 2 If Board[5] is blank, Go(5), else Go(1) Turn = 3 If Board[9] is blank, Go(9), else Go(3) Turn = 4 If Posswin(X)  0, then Go(Posswin(X)) ....... Unit1

  21. Introductory Problem: Tic-Tac-Toe Comments: 1. Not efficient in time, as it has to check several conditions before making each move. 2. Easier to understand the program’s strategy. 3. Hard to generalize. Unit1

  22. Introductory Problem: Tic-Tac-Toe 15- (8 + 5) Unit1

  23. Introductory Problem: Tic-Tac-Toe Comments: 1. Checking for a possible win is quicker. 2. Human finds the row-scan approach easier, while computer finds the number-counting approach more efficient. Unit1

  24. Introductory Problem: Tic-Tac-Toe Program 3: 1. If it is a win, give it the highest rating. 2. Otherwise, consider all the moves the opponent could make next. Assume the opponent will make the move that is worst for us. Assign the rating of that move to the current node. 3. The best node is then the one with the highest rating. Unit1

  25. Introductory Problem: Tic-Tac-Toe Comments: 1. Require much more time to consider all possible moves. 2. Could be extended to handle more complicated games. Unit1

  26. Introductory Problem: Question Answering “Mary went shopping for a new coat. She found a red one she really liked. When she got it home, she discovered that it went perfectly with her favourite dress”. Q1: What did Mary go shopping for? Q2: What did Mary find that she liked? Q3: Did Mary buy anything? Unit1

  27. Introductory Problem: Question Answering Program 1: 1. Match predefined templates to questions to generate text patterns. 2. Match text patterns to input texts to get answers. “What did X Y” “What did Mary go shopping for?” “Mary go shopping for Z” Z = a new coat Unit1

  28. Introductory Problem: Question Answering Program 2: Structured representation of sentences: Event2:Thing1: instance: Finding instance: Coat tense: Past colour: Red agent: Mary object: Thing 1 Unit1

  29. Introductory Problem: Question Answering Program 3: Background world knowledge: C finds M C leaves L C buys M C leaves L C takes M Unit1

  30. What is AI? Not about what human beings can do! About how to instruct a computer to do what human beings can do! Unit1

  31. Problems and Search Unit1

  32. Outline • State space search • Search strategies • Problem characteristics • Design of search programs Unit1

  33. State Space Search Problem solving = Searching for a goal state Unit1

  34. State Space Search: Playing Chess • Each position can be described by an 8-by-8 array. • Initial position is the game opening position. • Goal position is any position in which the opponent does not have a legal move and his or her king is under attack. • Legalmoves can be described by a set of rules: - Left sides are matched against the current state. - Right sides describe the new resulting state. Unit1

  35. State Space Search: Playing Chess • State space is a set of legal positions. • Starting at the initial state. • Using the set of rules to move from one state to another. • Attempting to end up in a goal state. Unit1

  36. State Space Search: Water Jug Problem “You are given two jugs, a 4-litre one and a 3-litre one. Neither has any measuring markers on it. There is a pump that can be used to fill the jugs with water. How can you get exactly 2 litres of water into 4-litre jug.” Unit1

  37. State Space Search: Water Jug Problem • State: (x, y) x = 0, 1, 2, 3, or 4 y = 0, 1, 2, 3 • Start state: (0, 0). • Goal state: (2, n) for any n. • Attempting to end up in a goal state. Unit1

  38. State Space Search: Water Jug Problem • (x, y) (4, y) if x  4 2. (x, y) (x, 3) if y  3 3. (x, y) (x  d, y) if x  0 4. (x, y) (x, y  d) if y  0 Unit1

  39. State Space Search: Water Jug Problem 5. (x, y) (0, y) if x  0 6. (x, y) (x, 0) if y  0 7. (x, y) (4, y  (4  x)) if x  y  4, y  0 8. (x, y) (x  (3  y), 3) if x  y  3, x  0 Unit1

  40. State Space Search: Water Jug Problem 9. (x, y) (x  y, 0) if x  y  4, y  0 10. (x, y) (0, x  y) if x  y  3, x  0 11. (0, 2) (2, 0) 12. (2, y) (0, y) Unit1

  41. State Space Search: Water Jug Problem • current state = (0, 0) 2.Loop until reaching the goal state (2, 0) -Apply a rule whose left side matches the current state - Set the new current state to be the resulting state (0, 0) (0, 3) (3, 0) (3, 3) (4, 2) (0, 2) (2, 0) Unit1

  42. State Space Search: Water Jug Problem The role of the condition in the left side of a rule restrict the application of the rule  more efficient 1. (x, y) (4, y) if x  4 2. (x, y) (x, 3) if y  3 Unit1

  43. State Space Search: Water Jug Problem Special-purpose rules to capture special-case knowledge that can be used at some stage in solving a problem 11. (0, 2) (2, 0) 12. (2, y) (0, y) Unit1

  44. State Space Search: Summary • Define a state space that contains all the possible configurations of the relevant objects. 2.Specify the initial states. 3.Specify the goal states. 4.Specify a set of rules: - What are unstated assumptions? - How general should the rules be? - How much knowledge for solutions should be in the rules? Unit1

  45. Search Strategies Requirements of a good search strategy: 1. It causes motion Otherwise, it will never lead to a solution. 2.It is systematic Otherwise, it may use more steps than necessary. 3. It is efficient Find a good, but not necessarily the best, answer. Unit1

  46. Search Strategies 1. Uninformed search (blind search) Having no information about the number of steps from the current state to the goal. 2.Informed search (heuristic search) More efficient than uninformed search. Unit1

  47. Search Strategies (0, 0) (4, 0) (0, 3) (4, 3) (0, 0) (1, 3) (4, 3) (0, 0) (3, 0) Unit1

  48. Search Strategies: Blind Search • Breadth-first search Expand all the nodes of one level first. • Depth-first search Expand one of the nodes at the deepest level. Unit1

  49. Search Strategies: Blind Search b: branching factor d: solution depth m: maximum depth Unit1

  50. Search Strategies: Blind Search b: branching factor d: solution depth m: maximum depth Unit1

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