Artificial Intelligence and Software that Learns and Evolves

1. Artificial Intelligence and Software that Learns and Evolves DIG 3563 – Fall 13 Dr. J. Michael Moshell University of Central Florida Adapted from A Special Presentation for Ajou University Autumn 2013 hplusmagazine.com

2. The Plan of the Lecture 0: What is a problem? What is intelligence? 1. The classical approach: logic and deduction 2. The knowledge-based approach: large databases 3. Cognitive science: models of human reasoning 4. Evolutionary Computing

3. 0: What is a Problem? "Something that is difficult to deal with." (Dictionary definition)

4. 0: What is a Problem? "Something that is difficult to deal with." (Dictionary definition) For a small child, this is a problem: Anna had $2.00. She spent $0.75 for candy. How much money does Anna have now? www.towngreendistrict.com

5. 0: What is a Problem? "Something that is difficult to deal with." (Dictionary definition) For a small child, this is a problem: Anna had $2.00. She spent $0.75 for candy. How much money does Anna have now? For the President of the United States, this is a problem: Can we change the laws so that everyone has a job, and the economy grows in a safe, steady fashion? www.nps.gov

6. Classifying Problems Problems Well-formulated problems other problems clear goals mixed goals limited action space infinite action space clear rules rules are changing

7. Classifying Problems Problems Well-formulated problems other problems clear goals mixed goals limited action space infinite action space clear rules rules are changing Easy Tractible Intractible problems problems problems kardwell.com en.wikipedia.org artsbeat.blog.nytimes.com

8. Tractible: definition Easily handled or worked. examples: Wood is a tractible material for making furniture. OPPOSITE: intractible Titanium is an intractible material for making furniture. bizchair.com worldchair.com

9. The Traveling Salesman Problem A man must visit 50 cities. He must visit each city ONE TIME. Find the shortest path for his travel.

10. The Traveling Salesman Problem A man must visit 50 cities. He must visit each city ONE TIME. Find the shortest path for his travel. A man must visit n cities. He must visit each city ONE TIME. Find the shortest path for his travel. How long to compute?

11. The Traveling Salesman Problem A man must visit 50 cities. He must visit each city ONE TIME. Find the shortest path for his travel. A man must visit n cities. He must visit each city ONE TIME. Find the shortest path for his travel. How long to compute? time = k c n (for some constants k and c). As n gets large, time gets VERY BIG VERY FAST

12. The Traveling Salesman Problem for k=1 microsecond and c=2, 50 cities takes 313,000 hours or 35 years!

13. Classifying Problems Problems Well-formulated problems other problems clear goals mixed goals limited action space infinite action space clear rules rules are changing Easy Tractible Intractible In 1975: problems problems problems kardwell.com en.wikipedia.org artsbeat.blog.nytimes.com

14. IBM's Deep BlueChess Playing Computer In 1989, IBM's computer and programming team defeated Garry Kasparov, world chess champion. It did not defeat the exponential time cost of chess. It simply made k and c small enough, and explored more futures than the human could. ibm.com en.wikipidia.org

15. Classifying Problems Problems Well-formulated problems other problems clear goals mixed goals limited action space infinite action space clear rules rules are changing Easy Tractible Intractible In 1990: problems problems problems kardwell.com en.wikipedia.org artsbeat.blog.nytimes.com

16. Decision Trees and Exponential Time-Cost trim-a-tree.co.uk Many problems are analyzed by building a decision tree and seeking a path to a winning node. Here, n=9 (nine options) en.wikipidia.org

17. Decision Trees and Exponential Time-Cost trim-a-tree.co.uk If each decision leads to a growing tree of other decisions, the time required to explore all the branches time = k c n and that is too long for anything but very small n. en.wikipidia.org

18. Heuristic: A plan tochoose options that are 'most likely to succeed' trim-a-tree.co.uk Eliminate those branches that your heuristic function tells you are not likely to succeed. Then expand the promising ones. en.wikipidia.org

19. Heuristic: A plan tochoose options that are 'most likely to succeed' trim-a-tree.co.uk A simple heuristic from chess: Do not exchange pieces if you lose more pawn-units than your opponent loses. Pawn=1 unit Knight, Bishop=3 pawns Rook=5 pawns Queen=9 pawns

20. Heuristic: A plan tochoose options that are 'most likely to succeed' trim-a-tree.co.uk A simple heuristic from chess: Do not exchange pieces if you lose more pawn-units than your opponent loses. Example: Do not exchange your queen for two knights. Pawn=1 unit Knight, Bishop=3 pawns Rook=5 pawns Queen=9 pawns

21. Intelligence = Problem Solving Ability? zmescience.com Most people agree that an intelligent agent must be able to solve some problems (not all problems.) However, Many people feel that if you have a well-formed problem, the hard work has already been done. The BIG challenge is transforming a real-world problem into a well-formedsymbolic problem.

22. Natural Language: a great place to find ill-formed problems zmescience.com Imagine a computer program that could answer questions: "Can a cat drive a car?" Computer and Program worldoffemale.com

23. Natural Language: a great place to find ill-formed problems zmescience.com Imagine a computer program that could answer questions: "Can a cat drive a car?" Computer and Program "No. A cat has no hands and cannot drive a car."

24. The Turing Test for Intelligence Alan Turing was a British mathematician who played a key role in World War II code-breaking and helped to develop the digital computer. He thought about intelligence and proposed a test. thocp.net

25. The Turing Test for Intelligence Is "mystery system" intelligent? Ask questions via a Teletype machine. Mystery System thocp.net Is "mystery system" a human or a machine? If you cannot accurately decide (and it's a machine) then the machine is intelligent. Mystery System

26. The Turing Test for Intelligence Has any system passed the Turing Test yet? Ask Siri ... Most people quickly conclude that Siri does not yet pass the Turing Test. But it's getting better all the time... scoopertino.com www.apple.com

27. 1. The Classical (Logical) Approach to Artificial Intelligence Basic concepts: 1. LOGIC is powerful enough to solve AI problems. 2. KNOWLEDGE must be represented in a formal system. 3. INFERENCE is the key mechanism to answer questions. All humans will die. John is a human therefore, John will die. hci.stanford.edu/~winograd

28. 1. The Classical (Logical) Approach to Artificial Intelligence Knowledge representation as a "semantic net" of related concepts hci.stanford.edu/~winograd en.wikipedia.org

29. 1. The Classical (Logical) Approach to Artificial Intelligence Example: Terry Winograd's SHRDLU System A "Toy world" of colored blocks (simulated by computer) Questions and commands (in English): 1) Translate into formal propositions 2) Try to prove or disprove them from the known facts. 3) Change system state if possible. hci.stanford.edu/~winograd University of Utah

30. 1. The Classical (Logical) Approach to Artificial Intelligence Example: Terry Winograd's SHRDLU System Person: Pick up a big red block Computer: OK Person: Grasp the pyramid hci.stanford.edu/~winograd University of Utah

31. 1. The Classical (Logical) Approach to Artificial Intelligence Example: Terry Winograd's SHRDLU System Person: Pick up a big red block Computer: OK Person: Grasp the pyramid Computer: I don't understand which pyramid you mean. (because there are two of them.) hci.stanford.edu/~winograd University of Utah

32. 1. The Classical (Logical) Approach to Artificial Intelligence Example: Terry Winograd's SHRDLU System Watch the SHRDLU movie (3 minutes 20 seconds of it) University of Utah

33. 1. The Classical (Logical) Approach to Artificial Intelligence Excitement! SHRDLU worked for Blocks World. followed by Disappointment: Most domains are MUCH harder. hci.stanford.edu/~winograd

34. 2. The Knowledge-Based Approach: Doug Lenat's talk at Google: Brittle Software (Lenat video: first 14 minutes)

35. 2. The Knowledge-Based Approach: Key concept: Today we have brittle (easily broken) software Danger: Power is in the hands of "smart idiots". Examples of Cyc's successes: Request: Find a picture of someone smiling  Cyc found picture of a man helping his daughter take her first step Request: Find something that could harm an airplane  Cyc located a video about an SA-7 missile

36. 2. The Knowledge-Based Approach: LARGE databases of facts. If SHRDLU's world was too small, let's build a big world of knowledge. Cyc Project – started in 1984 by Douglas Lenat Estimated effort (1986): 250,000 rules and 350 man-years of effott. Up until now: >1 million rules, and no end in sight.

37. 2. The Knowledge-Based Approach: LARGE databases of facts. If SHRDLU's world was too small, let's build a big world of knowledge. Cyc Project – started in 1984 by Douglas Lenat cYcorp distributes the OpenCyc 4.0 database (for free), with ~ 239,000 terms ~ 2,093,000 "triples" (rules) that attempt to represent human common sense.

38. 2. The Knowledge-Based Approach: cYcorp also has a private database with many more assertions and rules, in the CycL language. Example: (#$isa #$BillClinton #$UnitedStatesPresident) cycorp.org

39. Cyc: An example of the complexity cycorp.org University of Utah

40. Cyc: Method for Growing the Database * Attempt to automatically read encyclopedia articles. (enCYClopedia!) * Analyze successes & failures * Apply human "knowledge engineering" to improve rules

41. Cyc example: Terrorism Database * Analyze literature on terrorism * Predict future events. Success:  predicted anthrax mailings, 6 months before 9/11 Miss:  Predicted 1000 dolphins from Al-Qaeda to attack Hoover Dam www.usbr.gov

42. Cyc: Status and Hope for the Future Cyc will eventually become smart enough to teach itself. The results thus far: * Government sponsors basic research and terrorism database * Some commercial applications are being tried.

43. Cyc: Status and Hope for the Future Cyc will eventually become smart enough to teach itself. The results thus far: * Government sponsors basic research and terrorism database * Some commercial applications are being tried. * Many people in the Artificial Intelligence community doubt that Cyc will play a key role in successful AI Why? It's too logical. Humans are inconsistent, emotional, intuitive – they act on their FEELINGS --

44. 3. Cognitive Science – How Humans Think wikimedia commons

45. Philosophy: Example: Mind-Body Problem Is the mind part of the body? Or separate? Metaphors: "The brain is a telephone switchboard" "The brain is a computer"  Mind is software (can be changed) Brain is hardware (can be broken)  New ideas on good and evil

46. Philosophy: Example: Deductive Logic If A, and AB, then B A: A Hyundai is a car B: Cars are made by humans so: Hyundais are made by humans

47. Philosophy: Inductive logic: If the events in class C are probable, and A is in class C, then A is probable. 90% of humans are right-handed. Jack is a human. so Jack is probably right-handed.

48. Philosophy and Intelligence If a thing is intelligent, we expect it to use deductive logic and inductive logic.

49. Psychology: Definition: Study of mental functions and behaviors Example: Memory wikipedia.org/mimory

50. Psychology: Definition: Study of mental functions and behaviors Some types of long-term memory: Procedural (how to do something) everyculture.com