Artificial Intelligence

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Artificial Intelligence

• Introduction

Spring 2008, Juris Viksna
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Outline

• What is AI?
• Subjects covered in the course
• Requirements
• Textbooks
• Other practical information

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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?

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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

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What is AI?

• Intelligence encompasses abilities such as
• understanding language
• perception
• learning
• reasoning

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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

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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

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Pre-history of AI

• the quest for understanding automating
  intelligence has deep roots
• 4th cent. B.C. Aristotle studied mind thought,
  defined formal logic
• 14th16th 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)

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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
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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/plann
  er, means-end analysis
• Geometry Theorem Prover (Gelertner) input
  diagrams, backward reasoning
• SAINT(Slagle) symbolic integration, could pass
  MIT calculus exam

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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

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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

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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

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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

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History of AI
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Development of AI

• General Problem Solvers (1950s)
• Power (1960s)
• Romantic Period (mid 1960s to mid 1970s)
• Knowledge-based Approaches (mid 1970s to mid
  1990s)
• Biological and Social Models (mid 1990s to
  current)

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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"

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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

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Power

• Throw immense computing power at certain types of
  problems
• example CHESS
• examine 10120 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

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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.

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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.

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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.
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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 dont want me to notice that.
• ELIZA What makes you think I am not very
  aggressive?
• HUMAN You dont argue with me.
• ELIZA Why do you think I dont 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.

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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.

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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.

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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".

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Biological and Social Models

• Neural Networks (connectionist models in the text
  book)
• Based on the brains 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.

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Neural networks
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Neural networks
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Genetic algorithms
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Genetic algorithms
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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

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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

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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.)

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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.

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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)

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HALs 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, 1992.
My instructor was Mr. Langley and he taught me to
sing a song. If youd like to hear it, I can
sing it for you.
HALs last words, 2001 A Space Odyssey
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Turing test
AI system
Experimenter
Control
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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
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Weaknesses of the Turing Test

• The breadth of the test is nearly impossible to
  achieve.
• Some systems exhibit characteristics similar to
  Turings 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.

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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
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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.

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Does the person understand Chinese?

• Why?
• Why not?

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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.
• Searles 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.

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Searles 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.
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Goedels 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).
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Goedels Theorem
1, Px(x) terminates 0, otherwise
S(x)
Px(x) 1, Px(x) terminates 0, otherwise
T(x)
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Goedels Theorem
T Pk
Pk(k) 1, Pk(k) terminates 0, otherwise
Pk(k)
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Goedels Theorem
M - an intelligent program
Px(x) 1, Px(x) terminates 0 or does not
terminate, otherwise
M(x)
M Pk
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Goedels Theorem
M Pk - an intelligent program
Pk(k) 1, Pk(k) terminates 0 or, does
not terminate, otherwise
Pk(k)
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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.

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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.

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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

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Subjects covered in the course

• State space representations and search
  algorithms 3
• Decomposition spaces 2
• Game playing 2
• Automated reasoning (resolution methods) 3
• Neural networks 1
• Expert systems (???) 1
• 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.

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Requirements

• 2-4 theoretical homeworksMust be submitted
  before the exam session
• 40 for all homeworks
• Programming assignmentProblem to be announced
  early in MarchNo deadline must be submitted
  before the exam40
• Exam20
• Optional
• To qualify for grade 10 you may be asked to cope
  with some additional question(s)/problem(s)

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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
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Textbooks
Nils J. Nillson Problem-Solving Methods in
Artificial Intelligence McGraw-Hill, 1971.
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Textbooks
Nils J. Nillson Principles of Artificial
Intelligence Morgan Kaufmann, 1986
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Textbooks
Nils J. Nillson Artificial Intelligence a New
Synthesis   Morgan Kaufmann, 1998
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Textbooks
Rajan Shinghal Formal Concepts in Artificial
Intelligence Chapman Hall, 1992
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Textbooks
George F.Luger William A.Stubblefield Ronald
L.Rivest Artificial Intelligence and the
design of Expert Systems Benjamin/Cummings, 1989
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Textbooks
Elaine Rich Kevin Knight Artificial
Intelligence McGraw-Hill, 1991
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Textbooks
Judea Pearl Intelligent search strategies for
computer problem solving Addison-Wesley, 1984
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Textbooks
Nirmal .K.Bose Ping Liang Neural Network
Fundamentalswith Graphs, Algorithms and
Applications McGraw-Hill, 1996
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Textbooks
Roger Penrose The emperors new mind
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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

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Contact information
Juris Viksna Room 421, Rainis boulevard
29 phone [phone]