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CS 480 ARTIFICIAL INTELLIGENCE

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Direct line through mathematics and philosophy to modern AI. Problems: ... prediction problems, e.g., in determining the insurance premium for car etc. ... – PowerPoint PPT presentation

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Title: CS 480 ARTIFICIAL INTELLIGENCE


1
CS 480 ARTIFICIAL INTELLIGENCE
  • Instructor
  • B. Ravikumar
  • Computer Science Department
  • 116 I Darwin Hall
  • Class meets
  • Fridays 9 to 12

2
Course details
  • Catalog Description
  • A survey of techniques that simulate human
    intelligence. Topics may include pattern
    recognition, general problem solving, adversarial
    game-tree search, decision-making, expert
    systems, neural networks, fuzzy logic, and
    genetic algorithms. Prerequisite CS 315 or
    consent of instructor.
  • Background Expected
  • Programming and data structures (CS 315)
  • Discrete mathematics (CS 242)
  • Linear algebra
  • Some background in logic and probability will
    be helpful, but not necessary. 

3
Course details
  • Course Goals
  • AI covers wide range of topics
  • understanding language
  • vision and speech processing
  • problem solving, planning
  • common sense reasoning.
  • AI techniques
  • combinatorial (searching, A algorithm etc.)
  • logical (prove assertion in formal
    framework)
  • probabilistic (decision tree, Bayesian network)
  • machine learning (neural network, evolutionary
    technique)

4
Course details
  • Other references
  • N. Nillsson, AI A new synthesis.
  • Winston, Artificial Intelligence.

5
Course details

6
Course details
Short Quizzes (5 10) Two Mid-Term tests (20)
Both tests will be in class and will be about
75 miutes long. The tests will be open book/open
notes. Home Work and Projects (40 - 50)
There will be some common programming projects
and a final project. The final
project will be done individually. You can choose
a problem from a list that will be provided early
in the semester. The project is due the last week
of the semester. You are to write a report
summarizing your contributions to the chosen
problem. Some selected project work will be
presented in the department colloquium. Final
Examination (25 - 30) The final examination
will be comprehensive and will take place at the
scheduled time posted in the web page
http//www.sonoma.edu/university /classsched/
finals_sched.pdf (not updated for Fall 09 as of
August 15, 2009.)  

7
Lecture 1 Outline
  • Course overview
  • What is AI?
  • A brief history
  • The state of the art

Slides adapted from Russell and Norvig, AIAMA
8
Course overview
  • Introduction (chapters 1,2)
  • Techniques
  • Combinatorial (search) approach to AI (chapters
    3,4,5,6)
  • Symbolic (logical) approach to AI (chapters
    7,8,9)
  • Probabilistic approach to AI (chapters 13,14)
  • Learning approach to AI (chapters 18,20)
  • Applications
  • Natural Language Processing (chapter 22,23)
  • Computer vision (Chapter 24)

9
What is AI?
  • Authors think AI falls into four categories
  • Thinking humanly Thinking rationally
  • Acting humanly Acting rationally
  • The textbook advocates "acting rationally"



10
  • What is AI?
  • Before attempting a definition, we will state
    some major contemporary applications of AI
  • business advertising, financial decision making
  • web identifying objects in images, social
    network models etc.
  • medical image classification (belign vs.
    malignant tumor), image analysis using functional
    MRI
  • multiple field language translation, semantic
    analysis, speech synthesis, speech to text
    conversion.
  • industrial vision, robotics

11
Acting humanly Turing Test
  • Turing (1950) "Computing machinery and
    intelligence"
  • "Can machines think?" ? "Can machines behave
    intelligently?"
  • Operational test for intelligent behavior the
    Imitation Game
  • 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

12
Thinking humanly cognitive modeling
  • 1960s "cognitive revolution" information-processi
    ng psychology
  • Requires scientific theories of internal
    activities of the brain
  • How to validate? Requires
  • 1) Predicting and testing behavior of human
    subjects (top-down) or
  • 2) Direct identification from neurological
    data (bottom-up)
  • Both approaches (roughly, Cognitive Science and
    Cognitive Neuroscience)
  • now distinct from AI

13
Thinking rationally "laws of thought"
  • What are correct arguments/thought processes?
  • Several Greek schools developed various forms of
    logic notation and rules of derivation for
    thoughts may or may not have proceeded to the
    idea of mechanization
  • Direct line through mathematics and philosophy to
    modern AI
  • Problems
  • Not all intelligent behavior is mediated by
    logical deliberation
  • What is the purpose of thinking? What thoughts
    should I have?

14
Acting rationally rational agent
  • Rational behavior doing the right thing
  • The right thing that which is expected to
    maximize goal achievement, given the available
    information
  • Doesn't necessarily involve thinking e.g.,
    blinking reflex but thinking should be in the
    service of rational action

15
AI techniques
  • Combinatorial search problems
  • state space (over which search is performed)
  • finite state space (discrete)
  • how to move from one state to another (transition
    rules)
  • Applications
  • Games (one player or two players)
  • Navigation (robotics)
  • Solution
  • Search tree exploration

16
techniques
  • Combinatorial search approach
  • Sliding piece puzzle

1
2
3
1
2
3
4
6
4
5
6
Start
goal
8
7
5
7
8
Legal moves slide a piece next to empty
slot. Many AI problems can be modeled as search
problems.
17
A portion of a search tree for the 8-puzzle.
18
  • Combinatorial search
  • Uninformed search
  • depth-first
  • breadth-first
  • iterative deepening
  • breadth-depth
  • informed search
  • best-first

19
  • Combinatorial search
  • Depth-first search

What are the ways to speed-up DFS?
20
  • Combinatorial search
  • Breadth-first search

21
  • Combinatorial search
  • heuristic search
  • for each node, a heuristic provides an estimate
    of its distance from the goal.
  • for sliding-piece puzzle, Manhattan distance is
    one such estimate.
  • estimate for other search problems? (e.g. queen
    placement)

22
Combinatorial search
Consider placement in the 5th row.
23
techniques
  • Symbolic (logical) approach to AI
  • intelligent problem solving requires reasoning
    and deduction.
  • Knowledge is represented as a set of logical
    assertions A1, , An, and a conclusion to be
    drawn is also expressed as an assertion.
  • Can we deduce F from A1, , An?

24
Knowledge bases
  • Knowledge base set of sentences in a formal
    language
  • Declarative approach to building an agent (or
    other system)
  • Tell it what it needs to know
  • Then it can Ask itself what to do - answers
    should follow from the KB
  • Agents can be viewed at the knowledge level
  • i.e., what they know, regardless of how
    implemented
  • Or at the implementation level
  • i.e., data structures in KB and algorithms that
    manipulate them

25
Why knowledge-base
  • The state of the world
  • may require lots of information..
  • The agent knowledge of the state of the world
  • If s is world state K(s) is what the agent
    knows.
  • For economy
  • Not everything explicitly specified. Some facts
    can be inferred.
  • Agent may infer whatever he does not know
    explicitly.
  • Nillson Constraints on feature values
  • Block A is not on the floor
  • Issues
  • In what language to express what the agent knows
    about the world. How explicit to make this
    knowledge. How to infer.

Agent knowledge of state
Description of the world
Agent explicit specification of what he knows
26
The party example
  • If Alex goes, then Beki goes A ? B
  • If Chris goes, then Alex goes C ? A
  • Beki does not go not B
  • Chris goes C
  • Query Is it possible to satisfy all these
    conditions?
  • This is called satisfiability problem.

27
Example of languages
  • Programming languages
  • Formal languages, not ambiguous, but cannot
    express partial information. Not expressive
    enough.
  • Natural languages
  • Very expressive but ambiguous ex small dogs and
    cats.
  • Good representation language
  • Both formal and can express partial information,
    can accommodate inference
  • Main approach used in AI Logic-based languages.
  • Predicate-logic with Horn clauses

28
Deduction algorithms
Given P ? R, and Q ? R Can we deduce (P
Q)?
  • Example

Resolution strategy
  • Applications
  • expert systems (Mycin, dendral are early
    examples)
  • logic programming
  • automatic theorem proving (software validation)

29
Logical deduction in predicate logic
Example ?X (?Y ((mother(X) ? child_of(Y,X)) ?
loves(X,Y))) mother(mary) child_of(tom,mary) Can
we deduce? loves(tom, mary)
30
techniques
  • Probabilistic approach to AI
  • Knowledge representation models uncertainties.
  • Example
  • H Have a headache
  • F Coming down with Flu
  • P(H) 1/10
  • P(F) 1/40
  • P(HF) ½
  • Given that you have a headache, what is the
    probability that you have flu?
  • This kind of modeling is widely used in various
    prediction problems, e.g., in determining the
    insurance premium for car etc.

31
  • Probabilistic approach to AI
  • Some games are inherently probabilistic.
  • Financial markets
  • backgammon

32
techniques
Training set
New applicant (young, has job, does not own
house, good credit). Will (s)he default? We can
build a probabilistic model to answer.
33
techniques
  • Machine learning approach to AI
  • self-improving algorithms
  • solution obtained without explicit programming
  • Closer to modeling human intelligence or natural
    intelligence (we learn many things by observing
    even if step by step procedure absent)
  • Prominent examples
  • Neural networks
  • Genetic algorithms, evolutionary method

34
techniques
Neuron (very roughly modeled by neurons in human
brains.
35
techniques

An algorithm called back propagation algorithm is
used to adjust the weights of neurons based on
the discrepancy between correct output and
computed output.
36
  • techniques
  • Evolutionary algorithms
  • encoding of the collection of solutions as
    strings.
  • goal is to evolve the best solution.
  • use cross-over and mutation and iterate.

Example of cross-over and mutation
37
AI prehistory
  • Philosophy Logic, methods of reasoning, mind as
    physical system foundations of learning,
    language, rationality
  • Mathematics Formal representation and proof
    algorithms, computation, (un)decidability,
    (in)tractability, probability
  • Economics utility, decision theory
  • Neuroscience physical substrate for mental
    activity
  • Psychology phenomena of perception and motor
    control, experimental techniques
  • Computer building fast computers engineering
  • Control theory design systems that maximize an
    objective function over time
  • Linguistics knowledge representation, grammar

38
Abridged history of AI
  • 1943 McCulloch Pitts Boolean circuit
    model of brain
  • 1950 Turing's "Computing Machinery and
    Intelligence"
  • 1956 Dartmouth meeting "Artificial
    Intelligence" adopted
  • 1950s Early AI programs, including Samuel's
    checkers program, Newell Simon's Logic
    Theorist, Gelernter's Geometry Engine
  • 1965 Robinson's complete algorithm for logical
    reasoning
  • 196673 AI discovers computational
    complexity Neural network research almost
    disappears
  • 196979 Early development of knowledge-based
    systems
  • 1980-- AI becomes an industry
  • 1986-- Neural networks return to popularity
  • 1987-- AI becomes a science, probabilistic
    techniques
  • dominate
  • 1995-- The emergence of intelligent agents

39
State of the art
  • Deep Blue defeated the reigning world chess
    champion Garry Kasparov in 1997
  • Proved a mathematical conjecture (Robbins
    conjecture) unsolved for decades
  • No hands across America (driving autonomously 98
    of the time from Pittsburgh to San Diego)
  • During the 1991 Gulf War, US forces deployed an
    AI logistics planning and scheduling program that
    involved up to 50,000 vehicles, cargo, and people
  • NASA's on-board autonomous planning program
    controlled the scheduling of operations for a
    spacecraft
  • Proverb solves crossword puzzles better than most
    humans
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