Introduction to AI

1
Introduction to AI

• Russell and Norvig
• Chapter 1
• CMSC421 Fall 2006

2
Meta-Intro

• Personnel Myself, two TAs Galileo Namata and
  Vivek Sehgal
• Answers to some important questions
• What are the prerequisites for this course?
• How can I do well in this course?
• What are the course logistics?
• How do we stay awake in the late afternoon ??
• What will we learn in this class?

3
What are the prerequisites?

• Assume you know how to program. In addition, you
  should know
• basic algorithms, data structures and
  computational complexity
• i.e., searching graphs (DFS, BFS)
• lists, trees, graphs, etc.
• Difference between an O(n) and an O(2n) algorithm
  
• basic logic
• Truth table for x OR y, x AND y, x IMPLIES y
• basic probability
• P(A v B) P(A) P(B) P(A B)
• P(A B) P(A B) / P(B)

4
How do I do well in this course?

• Attend class
• Participate in class
• Do reading
• Suggestion 1) Set aside 20 minutes to skim
  chapter before lecture. 2) After lecture, go
  back and read the text in depth.
• Start written assignments early
• Assignments are not designed to be done the night
  before they are due
• Start programming assignments EARLY
• Do practice problems to study for exams
• Form study groups. Working together (not
  copying) is highly encouraged

More on this in a few slides
5
What are the course logistics?

• Web Page
• http//www.cs.umd.edu/class/fall2006/cmsc421/
• Mailing list
• http//mailman.cs.umd.edu/mailman/listinfo/cmsc421
  _2006
• Forum
• https//forum.cs.umd.edu/forumdisplay.php?f43

6
How do we stay awake?

• and learn something, ?!
• Course Ettiquette
• Arrive to class on time if you must leave during
  class, please try to limit the disruption/distract
  ion
• No cell phones, no side discussions
• No laptops during lectures
• Participate, Participate, Participate
• ask questions if you dont understand the
  material, probably there is someone else who does
  not either!
• some in class exercises
• Feedback
• please provide feedback
• there will be several opportunities, but also
  feel free to just come talk to me!

7
Summary Meta-Intro

• Answers to some important questions
• What are the prerequisites for this course?
• How can I do well in this course?
• What are the course logistics?
• How do we stay awake in the late afternoon ??
• What will we learn in this class?

8
What is AI?

• Class Exercise 0, part A
• On the 3x5 card youve been given, write down
  what your definition of AI
• You may also want to copy your definition to your
  notes, because youll be turning the card in
  (anonymously, no worries!)
• We will collect these definitions in 3 minutes!

9
Found on the Web

• AI is the simulation of intelligent human
  processes
• AI is the reproduction of the methods or results
  of human reasoning or intuition
• AI is the study of mental faculties through the
  use computational methods
• Using computational models to simulate
  intelligent behavior
• Machines to emulate humans

10
Why AI?

• Cognitive Science As a way to understand how
  natural minds and mental phenomena work
• e.g., visual perception, memory, learning,
  language, etc.
• Philosophy As a way to explore some basic and
  interesting (and important) philosophical
  questions
• e.g., the mind body problem, what is
  consciousness, etc.
• Engineering To get machines to do a wider
  variety of useful things
• e.g., understand spoken natural language,
  recognize individual people in visual scenes,
  find the best travel plan for your vacation, etc.

11
AI Characterizations

• Discipline that systematizes and automates
  intellectual tasks to create machines that

Think like humans Think rationally
Act like humans Act rationally
12
1 Act Like Humans

• Behaviorist approach
• Not interested in how you get results, just the
  similarity to what human results
• Exemplified by the Turing Test (Alan Turing,
  1950).

13
Turing Test

• Interrogator interacts with a computer and a
  person via a teletype.
• Computer passes the Turing test if interrogator
  cannot determine which is which.
• Loebner contest Modern version of Turing Test,
  held annually, with a 100,000 prize.
  http//www.loebner.net/Prizef/loebner-prize.html
• Participants include a set of humans and a set of
  computers and a set of judges.
• Scoring Rank from least human to most human.
• Highest median rank wins 2000.
• If better than a human, win 100,000. (Nobody
  yet)

14
2 Think Like Humans

• How the computer performs functions does matter
• Comparison of the traces of the reasoning steps
• Cognitive science ? testable theories of the
  workings of the human mind

• Exemplified by
• General Problem Solver (Newell and Simon)
• Neural networks
• Reinforcement learning

• But
• some early research conflated algorithm
  performance gt like human (and vice-versa)
• Do we want to duplicate human imperfections?

15
3 Thinking rationally

• Exemplified by "laws of thought"
• Aristotle what are correct arguments/thought
  processes?
• Several Greek schools developed various forms of
  logic notation and rules of derivation for
  thoughts
• Direct line through mathematics and philosophy to
  modern AI

• Problems
• Not easy to translate informal real world
  problem into formal terms (problem formulation is
  difficult)
• While may be able to solve the problem in
  principal (i.e. decidable), in practice, may not
  get the answer in a reasonable amount of time
  (computationally intractable)

16
4 Acting Rationally

• Rational behavior do the right thing
• Always make the best decision given what is
  available (knowledge, time, resources)
• Perfect knowledge, unlimited resources ? logical
  reasoning (3)
• Imperfect knowledge, limited resources ?
  (limited) rationality

• Connection to economics, operational research,
  and control theory
• But ignores role of consciousness, emotions,
  fear of dying on intelligence

17
AI Characterizations

• Discipline that systematizes and automates
  intellectual tasks to create machines that

2 Think like humans 3 Think rationally
1 Act like humans 4 Act rationally
18
What is AI?

• Class Exercise 0, part B
• Hopefully you have received the AI definition
  from another student in the course.
• Break into groups of 4 people
• In your groups
• Start by giving a quick introduction name, year,
  etc.
• On the additional blank card each group has been
  given, write each person in the groups name and
  email
• Each person has a card read the card to the
  group, and the group should decide the category

2 Think like humans 3 Think rationally
1 Act like humans 4 Act rationally
19
Bits of History

• 1956 The name Artificial Intelligence was
  coined by John McCarthy. (Would computational
  rationality have been better?)
• Early period (50s to late 60s) Basic
  principles and generality
• General problem solving
• Theorem proving
• Games
• Formal calculus

20
Bits of History

• 1969-1971 Shakey the robot (Fikes, Hart,
  Nilsson)
• Logic-based planning (STRIPS)
• Motion planning (visibility graph)
• Inductive learning (PLANEX)
• Computer vision

21
Bits of History

• Knowledge-is-Power period (late 60s to mid
  80s)
• Focus on narrow tasks require expertise
• Encoding of expertise in rule formIf the car
  has off-highway tires and 4-wheel drive
  and high ground clearanceThen the car can
  traverse difficult terrain (0.8)
• Knowledge engineering
• 5th generation computer project
• CYC system (Lenat)

22
Bits of History

• AI becomes an industry (80s present)
• Expert systems Digital Equipment, Teknowledge,
  Intellicorp, Du Pont, oil industry,
• Lisp machines LMI, Symbolics,
• Constraint programming ILOG
• Robotics Machine Intelligence Corporation,
  Adept, GMF (Fanuc), ABB,
• Speech understanding
• Information Retrieval Google,

23
Predictions and Reality (1/3)

• In the 60s, a famous AI professor from MIT said
  At the end of the summer, we will have developed
  an electronic eye
• As of 2002, there is still no general computer
  vision system capable of understanding complex
  dynamic scenes
• But computer systems routinely perform road
  traffic monitoring, facial recognition, some
  medical image analysis, part inspection, etc

24
Predictions and Reality (2/3)

• In 1958, Herbert Simon (CMU) predicted that
  within 10 years a computer would be Chess
  champion
• This prediction became true in 1998
• Today, computers have won over world champions in
  several games, including Checkers, Othello, and
  Chess, but still do not do well in Go

25
Predictions and Reality (3/3)

• In the 70s, many believed that
  computer-controlled robots would soon be
  everywhere from manufacturing plants to home
• Today, some industries (automobile, electronics)
  are highly robotized, but home robots are still a
  thing of the future
• But robots have rolled on Mars, others are
  performing brain and heart surgery, and humanoid
  robots are operational and available for rent
  (see http//world.honda.com/news/2001/c011112.htm
  l)

26
State of the Art

• Drive safely along a curving mountain road
• Drive safely along US 1
• Buy a weeks worth of groceries on the web
• Buy a weeks worth of groceries at your local
  Giant
• Play a decent game of bridge
• Write an intentionally funny story
• Give competent legal advice in a specialized area
  of law
• Translate spoken English into spoken Swedish in
  real time
• Converse successfully with another person for an
  hour
• Play a decent game of ping-pong
• Unload any dishwasher and put everything away
• Explore the Martian landscape
• Explore downtown Washington

27
Why is AI Hard?

• Simple syntactic manipulation is not enough

• Machine Translation
• Big project in 1957 following Sputnik launch
• Translation of Russian documents
• The spirit is willing but the flesh is weak
• The vodka is strong but the meat is rotten

28
Eliza

• ELIZA A program that simulated a psychotherapist
  interacting with a patient
• Coded at MIT during 1964-1966 by Joel Weizenbaum.
• First script was DOCTOR.
• The script was a simple collection of syntactic
  patterns not unlike regular expressions
• Each pattern had an associated reply which might
  include bits of the input (after simple
  transformations (my ? your)
• Weizenbaum was shocked at reactions
• Psychiatrists thought it had potential.
• People unequivocally anthropomorphized.
• Many thought it solved the NL problem.
• Try typing Meta-x doctor in Emacs.
• Recent Loebner Prize winner, http//www.alicebot.o
  rg/

29
Colbys PARRY
H Good afternoon. My name is Doctor Jones. P
Pleased to meet you. H How are you? P All
right I guess. H Could you tell me your name?
P Pat Smith. H I though you might have some
problems. P I've been upset lately. H By
what? P People get on my nerves sometimes. H
Tell me more. P Do you know anything about
bookies? ...

• Kenneth Colby modeled a paranoid using the same
  techniques circa 1968.
• PARRY has basic emotions. If it gets angry, its
  replies become more hostile.
• In a Turing test, expert psychiatrists were
  unable to distinguish PARRYs ramblings from
  those of real paranoids.

30
Parry meets Eliza

• P I don't understand your motives.
• E Why don't you understand my motives?
• P Shouldn't I be?
• E Why do you say that just now?
• P That's just the way it is.
• E Suppose that's the way it were not.
• P So?
• E I am not sure I understand you fully.
• P You harp on the same questions too much.
• E What does that similarity suggest to you?
• P I don't get you.
• E Do you wish that you did get I?

Please tell me this isn't AI!
31
Why is AI Hard?

• Computational intractability

• AI goal defined before notion of NP-completeness
• people thought to solve larger problems we
  simply need larger/faster computers
• didnt understand the notion of exponential
  growth

ALL of the algorithms we will study will be
computational intractable (NP-complete as best)
in the WORST case How does the fact that we
are dealing with the REAL WORLD make solving
these computationally challenging problems
feasible IN PRACTICE?
32
CMSC 421

• We will focus on the rational agents
  (engineering) paradigm
• Make computers act more intelligently
• Three major components
• representation
• reasoning
• learning

33
Course overview

• Introduction and Agents (chapters 1,2)
• Search (chapters 3,4,5,6)
• Logic (chapters 7,8,9)
• Planning (chapters 11,12)
• Uncertainty (chapters 13,14,15,16,17)
• Learning (chapters 18,20,21)

34
Learning Goals for Class

• You will learn a bunch of tools that are useful
  for building useful, adaptive software to solve
  fun and challenging problems
• These tools will be useful for you whether you go
  into AI research (basics that anyone should know)
  or any other discipline (oh, hey, that looks like
  the planning problems we studied way back in
  cmsc421)
• Help you separate hype from whats easily
  achievable using existing tools (and avoid
  reinventing them!)