1. Artificial Intelligence
Introduction
Spring 2008, Juris Vīksna

2. Outline What is AI? Subjects covered in the course Requirements
Textbooks
Other practical information

3. What is AI? General definition:
AI is the branch of computer science that is concerned with the automation of intelligent behavior.
what is intelligent behavior?
is intelligent behavior the same for a computer and a human?

4. What is AI? Tighter definition:
AI is the science of making machines do things that would require intelligence if done by people. (Minsky)
at least we have experience with human intelligence
possible definition: intelligence is the ability to form plans to achieve goals by interacting with an information-rich environment

5. What is AI? Intelligence encompasses abilities such as:
understanding language
perception
learning
reasoning

6. What is AI? Self-defeating definition:
AI is the science of automating intelligent behaviors currently achievable by humans only.
this is a common perception by the general public
as each problem is solved, the mystery goes away and it's no longer "AI"
successes go away, leaving only unsolved problems

7. What is AI? Self-fulfilling definition:
AI is the collection of problems and methodologies studied by AI researchers.
AI ranges across many disciplines
computer science, engineering, cognitive science, logic, …
research often defies classification, requires a broad context

8. Pre-history of AI
the quest for understanding & automating intelligence has deep roots
4th cent. B.C.: Aristotle studied mind & thought, defined formal logic
14th–16th cent.: Renaissance thought built on the idea that all natural or artificial processes could be mathematically analyzed and understood
18th cent.: Descartes emphasized the distinction between mind & brain (famous for "Cogito ergo sum")
19th cent.: advances is science & understanding nature made the idea of creating artificial life seem plausible
Shelley's Frankenstein raised moral and ethical questions
Babbage's Analytical Engine proposed a general-purpose, programmable computing machine -- metaphor for the brain
19th-20th cent.: saw many advances in logic formalisms, including Boole's algebra, Frege's predicate calculus, Tarski's theory of reference
20th cent.: advent of digital computers in late 1940's made AI a viable
Turing wrote seminal paper on thinking machines (1950)

9. Pre-history of AI
birth of AI occurred when Marvin Minsky & John McCarthy organized the Dartmouth Conference in 1956
brought together researchers interested in "intelligent machines"
for next 20 years, virtually all advances in AI were by attendees
Minsky (MIT), McCarthy (MIT/Stanford), Newell & Simon (Carnegie),…
Marvin Minsky
John McCarthy

10. History of AI the history of AI research is a continual cycle of
optimism & hype  reality check & backlash  refocus & progress  …
1950's – birth of AI, optimism on many fronts
general purpose reasoning, machine translation, neural computing, …
first neural net simulator (Minsky): could learn to traverse a maze
GPS (Newell & Simon): general problem-solver/planner, means-end analysis
Geometry Theorem Prover (Gelertner): input diagrams, backward reasoning
SAINT(Slagle): symbolic integration, could pass MIT calculus exam

11. History of AI
1960's – failed to meet claims of 50's, problems turned out to be hard!
so, backed up and focused on "micro-worlds"
within limited domains, success in: reasoning, perception, understanding, …
ANALOGY (Evans & Minsky): could solve IQ test puzzle
STUDENT (Bobrow & Minsky): could solve algebraic word problems
SHRDLU (Winograd): could manipulate blocks using robotic arm, explain self
STRIPS (Nilsson & Fikes): problem-solver planner, controlled robot "Shakey"
Minsky & Papert demonstrated the limitations of neural nets

12. History of AI
1970's – results from micro-worlds did not easily scale up
so, backed up and focused on theoretical foundations, learning/understanding
conceptual dependency theory (Schank)
frames (Minsky)
machine learning: ID3 (Quinlan), AM (Lenat)
practical success: expert systems
DENDRAL (Feigenbaum): identified molecular structure
MYCIN (Shortliffe & Buchanan): diagnosed infectious blood diseases

13. History of AI 1980's – BOOM TOWN!
cheaper computing made AI software feasible
success with expert systems, neural nets revisited, 5th Generation Project
XCON (McDermott): saved DEC ~ $40M per year
neural computing: back-propagation (Werbos), associative memory (Hopfield)
logic programming, specialized AI technology seen as future

14. History of AI 1990's – again, failed to meet high expectations
so, backed up and focused : embedded intelligent systems, agents, …
hybrid approaches: logic + neural nets + genetic algorithms + fuzzy + …
CYC (Lenat): far-reaching project to capture common-sense reasoning
Society of Mind (Minsky): intelligence is product of complex interactions of simple agents
Deep Blue (formerly Deep Thought): defeated Kasparov in Speed Chess in 1997

15. History of AI

16. Development of AI General Problem Solvers (1950’s) Power (1960’s)
“Romantic” Period (mid 1960’s to mid 1970’s)
Knowledge-based Approaches (mid 1970’s to mid 1990’s)
Biological and Social Models (mid 1990’s to current)

17. General problem solvers
use a generalized problem solving method (divide up problems, work forward, work backward) and apply approach to a VERY BROAD range of problems.
limitations:
hardware capabilities
sometimes called "weak solution methods"

18. Examples of General Problem Solvers
LOGIC THEORIST
could prove 38 of the first 52 theorems in the Principia Mathematica
Journal of Symbolic Logic declined publishing an article with Logic Theorist as a co-author
GENERAL PROBLEM SOLVER
used means-ends analysis to reduce the difference between the current state and desired (end) state
handled mathematical logic problems, hence not as general as originally hoped

19. Power Throw immense computing power at certain types of problems
example: CHESS
examine moves to consider all possibilities.
advances that made this approach possible:
more powerful hardware
languages developed specifically for AI applications Examples of General Problem Solvers

20. Romantic period Focus on computer understanding Hence concentrated on:
natural language - particularly stories and dialog.
Fundamental problem:
what is "understanding"?
Can a computer be said to understand things?
Not clear how humans understand things, for instance reading in young children.

21. Examples of Romantic Period
SHRDLU - developed by Winograd
understood and reasoned about its block world
ELIZA - Rogerian therapist
Newell and Simon examined human problem solving via protocol analysis to determine how humans understand things. Then created computer programs to generate a similar trace.

22. SHRLDU SHRLDU has just completed the command:
“Find a block which is taller than the one you are holding and put on the box”
Example of microworld.

23. Eliza sample dialog HUMAN: You are like my father in some ways.
ELIZA: What resemblance do you see?
HUMAN: You are not very aggressive, but I think you don’t want me to notice that.
ELIZA: What makes you think I am not very aggressive?
HUMAN: You don’t argue with me.
ELIZA: Why do you think I don’t argue with you?
HUMAN: You are afraid of me.
ELIZA: Does it please you to believe I am afraid of you?
HUMAN: My father is afraid of everybody.
ELIZA: What else comes to mind when you think of your father?
HUMAN: Bullies.

24. Knowledge based approaches
Flaws of previous approaches considered:
General problem solving tries to apply a single solution approach to a wide range of problems. The general approaches were not as general as hoped and more problem specific approaches could be more powerful and simpler.

25. Knowledge based approaches
Power approach tried to program optimal (highest probability) approach. Human experts use HEURISTICS (rules of thumb) to find a solution.
Example: Chess masters don't look ahead very many moves, as a POWER approach implies. Instead they choose from a set of ‘good’ alternatives.

26. Knowledge based approaches
Romantic period: true understanding may not be necessary to achieve useful results.
Feigenbaum, in a speech at Carnegie, challenged his former professors to stop looking at "toy problems" and apply AI techniques to "real problems".
The key to solving real world problems is that these system handle only a very specific problem area, a "narrow domain".

27. Biological and Social Models
Neural Networks (connectionist models in the text book)
Based on the brain’s ability to adapt to the world by modifying the relationships between neurons.
Genetic algorithms attempt to replicate biological evolution.
Populations of competing solutions are generated.
Poor solutions die out, better ones survive and reproduce with ‘mutations’ created.
Software agents
Semi-autonomous agents, with little knowledge of other agents solve part of a problem, which is reported to other agents.
Through the efforts of many agents a problem is solved.

28. Neural networks

29. Neural networks

30. Genetic algorithms

31. Genetic algorithms

32. Philosophical extremes in AI
Neats vs. Scruffies
Neats focus on smaller, simplified problems that can be well-understood, then attempt to generalize lessons learned
Scruffies tackle big, hard problems directly using less formal approaches
GOFAIs vs. Emergents
GOFAI (Good Old-Fashioned AI) works on the assumption that intelligence can and should be modeled at the symbolic level
Emergents believe intelligence emerges out of the complex interaction of simple, sub-symbolic processes

33. Philosophical extremes in AI
Weak AI vs. Strong AI
Weak AI believes that machine intelligence need only mimic the behavior of human intelligence
Strong AI demands that machine intelligence must mimic the internal processes of human intelligence, not just the external behavior

34. Different views of AI Strong view
The effort to develop computer-based systems that behave as humans.
Argues that an appropriately programmed computer really is a mind, that understands and has cognitive states.
“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate.” (From Dartmouth conference.)

35. Different views of AI Weak view
Use “intelligent” programs to test theories about how human beings carry out cognitive operations.
AI is the study of mental faculties through the use of computational models.
Computer-based system that acts in such a way (i.e., performs tasks) that if done by a human we would call it ‘intelligent’ or ‘requiring intelligence’.

36. Criteria for success long term: Turing Test (for Weak AI)
as proposed by Alan Turing (1950), if a computer can make people think it is human (i.e., intelligent) via an unrestricted conversation, then it is intelligent
Turing predicted fully intelligent machines by 2000, not even close
Loebner Prize competition, extremely controversial
short term: more modest success in limited domains
performance equal or better than humans
e.g., game playing (Deep Blue), expert systems (MYCIN)
real-world practicality $$$
e.g., expert systems (XCON, Prospector), fuzzy logic (cruise control)

37. HAL’s last words, “2001: A Space Odyssey”
“Good afternoon, gentleman. I am HAL 9000 computer. I became operational at the HAL plant in Urbana, Ill., on the 12th of January, My instructor was Mr. Langley and he taught me to sing a song. If you’d like to hear it, I can sing it for you.”
HAL’s last words, “2001: A Space Odyssey”

38. Turing test
AI system
Experimenter
Control

39. Appeal of the Turing Test
Provides an objective notion of intelligence, i.e., compare intelligence of the system to something that is considered intelligent, avoiding debates over what is intelligence.
Avoids debates of whether or not the system uses correct internal processes.
Eliminates biases toward living organisms since experimenter communicates with both the AI system and the control (human) in the same manner.
Alan Turing

40. Weaknesses of the Turing Test
The breadth of the test is nearly impossible to achieve.
Some systems exhibit characteristics similar to Turing’s criteria, yet we would not label them ‘intelligent;’ e.g., ELIZA is easy to unmask, it cannot pass a true interrogation.
Focuses on symbolic, problem solving ignores perceptual skills and manual dexterity which are important components of human intelligence.
By focusing on replicating human intelligence, researchers may be distracted from the tasks of developing theories that explain the mechanisms of human and machine intelligence and applying the theories to solving actual problems.

41. The Chinese Room She does not know Chinese Correct Responses
Chinese Writing is given to the person
Set of rules, in English, for transforming phrases

42. The Chinese Room Scenario
An individual is locked in a room and given a batch of Chinese writing. The person locked in the room does not understand Chinese.
Next she is given more Chinese writing and a set of rules (in English which she understands) on how to collate the first set of Chinese characters with the second set of Chinese characters.
If the person becomes good at manipulating the Chinese symbols and the rules are good enough, then to someone outside the room it appears that the person understands Chinese.

43. Does the person understand Chinese?
Why?
Why not?

44. The Chinese Room (cont.)
Searle's, who developed the argument, point is that she doesn't really understand Chinese, she really only follows a set of rules.
Following this argument, a computer could never be truly intelligent, it is only manipulates symbols. The computer does not understand the semantic context.
Searle’s criteria is “intentionality,” the entity must be intentionally exhibiting the behavior, not simply following a set of rules.
Intentionality is as difficult to define as intelligence.
Searle excludes ‘weak AI’ from his argument against the possibility of AI.

45. Searle’s argument created a huge response
This religious diatribe against AI, masquerading as a serious scientific argument, is one of the wrongest, most infuriating articles I have ever read in my life I know that this journal is not the place for philosophical and religious commentary, yet it seems to me that what Searle and I have is, at the deepest level, a religious disagreement and I doubt that anything I say could ever change his mind. He insists on things he calls "causal intentional properties" which seem to vanish as soon as you analyze them, find rules for them, or simulate them. But what those things are, other than epiphenomena, or innocently emergent qualities I don't know.

46. Goedel’s Theorem The halting problem
For a given computer program P and given input data x,
output “yes” if the computation P(x) terminates and
output “no” otherwise.
The halting problem is undecidable (i.e. it is not solvable
by any computer program).

47. Goedel’s Theorem 1, Px(x) terminates 0, otherwise S(x) =
Px(x) + 1, Px(x) terminates
0, otherwise
T(x) =

48. Goedel’s Theorem T = Pk Pk(k) + 1, Pk(k) terminates 0, otherwise

49. Goedel’s Theorem M - an “intelligent” program
Px(x) + 1, Px(x) terminates
0 or does not terminate, otherwise
M(x) =
M = Pk

50. Goedel’s Theorem M = Pk - an “intelligent” program
Pk(k) + 1, Pk(k) terminates
0 or,
does not terminate, otherwise
Pk(k) =

51. What is artificial intelligence?
Arguments about AI seem to rapidly break down into philosophical debates where there is probably no absolute right or wrong answer.
Note Hofstadter's comments about "religious" disagreement. It often comes down to considering the pros and cons of both sides, realizing that neither is completely right (or completely wrong) and taking a stand for one or the other.
Which side you tend to fall on will, almost unavoidably, be based on personal values.

52. Summary No universally accepted definition of intelligence.
Definitions of intelligence is subject to change, which makes it difficult to aim for! Similar to the situation in linguistics and for comparative psychologists that have taught primates sign language.
"The Ultimate Limits of AI” - notice that these are really sociological questions.
This course will focus what has been achieved in AI. However, be aware of these issues.

53. Branches of AI Games - study of state space search, e.g., chess
Automated reasoning and theorem proving, e.g., logic theorist
Expert/Knowledge-based systems
Natural language understanding and semantic modeling
Model human cognitive performance
Robotics and planning
Automatic programming
Learning
Vision

54. Subjects covered in the course
State space representations and search algorithms 3
Decomposition spaces
Game playing
Automated reasoning (resolution methods) 3
Neural networks
Expert systems (???)
Learning (Decision trees, Genetic algorithms, HMMs) 3
“Typical” AI subjects likely not to be covered
Natural language processing
Knowledge representation
Planning systems
“AI programming languages” - LISP, PROLOG etc.

55. Requirements
2-4 theoretical homeworks Must be submitted before the exam session
40% for all homeworks
Programming assignment Problem to be announced early in March No deadline – must be submitted before the exam 40%
Exam 20%
Optional
To qualify for grade 10 you may be asked to cope with some additional question(s)/problem(s)

56. Academic honesty You are expected to submit only your own work!
Sanctions:
Receiving a zero on the assignment (in no circumstances
a resubmission will be allowed)
No admission to the exam and no grade for the course

57. Textbooks Nils J. Nillson Problem-Solving Methods in
Artificial Intelligence
McGraw-Hill, 1971.

58. Textbooks Nils J. Nillson Principles of Artificial Intelligence
Morgan Kaufmann, 1986

59. Textbooks Nils J. Nillson Artificial Intelligence: a New Synthesis
Morgan Kaufmann, 1998

60. Textbooks Rajan Shinghal Formal Concepts in Artificial Intelligence
Chapman & Hall, 1992

61. Textbooks George F.Luger William A.Stubblefield Ronald L.Rivest
Artificial Intelligence and
the design of Expert Systems
Benjamin/Cummings, 1989

62. Textbooks Elaine Rich Kevin Knight Artificial Intelligence
McGraw-Hill, 1991

63. Textbooks Judea Pearl Intelligent search strategies
for computer problem solving
Addison-Wesley, 1984

64. Textbooks Nirmal .K.Bose Ping Liang Neural Network
Fundamentalswith Graphs,
Algorithms and Applications
McGraw-Hill, 1996

65. Textbooks
Roger Penrose
The emperors new mind

66. Web page http://susurs.mii.lu.lv/juris/courses/ai2008.html
Is expected to contain:
short summaries of lectures
announcements
power point presentations (when available)
homework and programming assignment problems
your grades (???)
other relevant information

67. Contact information
Juris Vīksna
Room 421, Rainis boulevard 29
phone: