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Bil682 - Yapay Anlayis Artificial Intelligence


Bil682 - Yapay Anlay Artificial Intelligence G z 2011 Dr. Nazl kizler Cinbi Slides mostly adapted from AIMA What is AI? A system is rational if it does the ... – PowerPoint PPT presentation

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Title: Bil682 - Yapay Anlayis Artificial Intelligence

Bil682 - Yapay Anlayis Artificial Intelligence
  • Güz 2011
  • Dr. Nazli Ikizler Cinbis
  • Slides mostly adapted from AIMA

What is AI?
Thinking humanly The exiting new effort to make computers thinkmachines with minds, in the full and literal sense (Haugeland, 1985) The automation of activities that we associate with human thinking, activities such as decision making, problem solving, learning (Bellman, 1978) Thinking rationally The study of mental faculties through the use of computational models (Charniak and McDermott, 1985) The study of the computations that make it possible to perceive, reason and act (Winston, 1992)
Acting humanly The art of creating machines that perform functions that require intelligence when performed by people (Kurzweil, 1990) The study of how to make computers do things at which, at the moment, people are better (Rich and Knight, 1991) Acting rationally Computational Intelligence is the study of the design of intelligent agents (Poole et. al, 1998) AI is concerned with intelligent behaviour in artifacts (Nilson, 1998)
  • A system is rational if it does the right thing
    given what it knows

Acting humanly Turing Test
  • Alan Turing (1950) "Computing machinery and
  • Operational test for intelligent behavior the
    Imitation Game
  • The computer passes the test if a human
    interrogator, after posing some written
    questions, cannot tell whether the written
    responses come from a person or not. If the
    response of a computer to an unrestricted textual
    natural-language conversation cannot be
    distinguished from that of a human being then it
    can be said to be intelligent.
  • Suggested major components of AI Natural
    Language Processing, Knowledge Representation,
    Automated Reasoning, Machine Learning
  • Total Turing Test requires Computer Vision and
    Robotics as well

Thinking humanly cognitive modeling
  • In order to say that a given program thinks like
    a human, we must have some way of determining how
    humans think
  • Requires scientific theories of internal
    activities of the brain
  • -- How to validate? Requires
  • 1) Cognitive Science Predicting and testing
    behavior of human subjects (top-down)
  • or 2) Cognitive Neuroscience Direct
    identification from neurological data (bottom-up)
  • Both approaches (roughly, Cognitive Science and
    Cognitive Neuroscience) are now distinct from AI

Thinking rationally "laws of thought"
  • Aristotle what are correct arguments/thought
  • 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
  • Formalize correct reasoning using a
    mathematical model(e.g. of deductive reasoning).
  • Direct line through mathematics and philosophy to
    modern AI logicist tradition hopes to build
    logic programs to create intelligent systems
  • Problems
  • Informal knowledge may not be represented in
    formal terms of logic.
  • Solving problems in principle is different than
    solving in practice.

Acting rationally
  • Rational behavior doing the right thing
  • The right thing that which is expected to
    maximize goal achievement, given the available
  • Doesn't necessarily involve thinking e.g.,
    blinking reflex
  • But thinking should be in the service of rational
  • Entirely dependent on goals
  • Irrational ! insane, irrationality is a
    suboptimal action
  • Rational ! successful

Rational agents
  • An agent is an entity that perceives its
    environment and is able to execute actions to
    change it.
  • Abstractly, an agent is a function from percept
    histories to actions
  • f P ? A
  • For any given class of environments and tasks, we
    seek the agent (or class of agents) with the best

Rational agents
  • Agents have inherent goals that they want to
    achieve (e.g. survive, reproduce).
  • A rational agent acts in a way to maximize the
    achievement of its goals.
  • True maximization of goals requires omniscience
    and unlimited computational abilities.
  • In real world, usually lots of uncertainty
  • Usually, were just approximating rationality
  • ? design best program for given machine resources
    and available knowledge

Foundations of AI
Many older disciplines contribute to AI
  • 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
  • Psychology phenomena of perception and motor
    control, experimental techniques
  • Computer Science hardware, algorithms,
    computational complexity theory, fast
  • Control theory design systems that maximize an
    objective function over time
  • Linguistics knowledge representation, grammar,
    syntax, semantics

Abridged history of AI
  • 1943 McCulloch Pitts Boolean circuit
    model of brain
  • 1950 Turing's "Computing Machinery and
  • 1956 Dartmouth meeting "Artificial
    Intelligence" adopted
  • 195269 Look, Ma, no hands!
  • 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
  • 1960s Work in the sixties at MIT lead by Marvin
    Minsky and John McCarthy
  • Development of LISP symbolic programming language
  • SAINT Solved freshman calculus problems
  • ANALOGY Solved IQ test analogy problems
  • SIR Answered simple questions in English
  • STUDENT Solved algebra story problems
  • SHRDLU Obeyed simple English commands in
    theblocks world

Abridged history of AI
  • 196673 AI discovers computational
    complexity Neural network research almost
  • 196979 Early development of knowledge-based
  • 1980-- AI becomes an industry
  • 1986-- Neural networks return to popularity
  • 1987-- AI becomes a science
  • 1995-- The emergence of intelligent agents

Early Limitations
  • Hard to scale solutions to toy problems to more
    realistic ones due to difficulty of formalizing
    knowledge and combinatorial explosion of search
    space of potential solutions.
  • Limitations of Perceptron demonstrated by Minsky
    and Papert (1969).

Slide from R. Mooney
Knowledge is Power Expert Systems
  • Discovery that detailed knowledge of the speci?c
    domain can help control search and lead to expert
    level performance for restricted tasks.
  • First expert system DENDRAL for interpreting mass
    spectrogram data to determine molecular structure
    by Buchanan, Feigenbaum, and Lederberg (1969).
  • Early expert systems developed for other tasks
  • MYCIN diagnosis of bacterial infection (1975)
  • PROSPECTOR Found molybendum deposit based on
    geological data (1979)
  • R1 Con?gure computers for DEC (1982)

Slide from R. Mooney
AI Industry
  • Development of numerous expert systems in early
  • Estimated 2 billion industry by 1988.
  • Japanese start Fifth Generation project in 1981
    to build
  • intelligent computers based on Prolog logic
  • MCC established in Austin in 1984 to counter
    Japanese project.
  • Limitations become apparent, prediction of AI
  • Brittleness and domain speci?city
  • Knowledge acquisition bottleneck

Slide from R. Mooney
Rebirth of Neural Networks
  • New algorithms (e.g. backpropagation) discovered
    for training more complex neural networks (1986).
  • Cognitive modeling of many psychological
    processes using neural networks, e.g. learning
  • Industrial applications
  • Character and hand-writing recognition
  • Speech recognition
  • Processing credit card applications
  • Financial prediction
  • Chemical process control

Slide from R. Mooney
What Can AI Do?
  • Quiz Which of the following can be done at
  • Play a decent game of table tennis?
  • Drive safely along a curving mountain road?
  • Drive safely along busy traffic?
  • Buy a week's worth of groceries on the web?
  • Discover and prove a new mathematical theorem?
  • Converse successfully with another person for
    an hour?
  • Perform a complex surgical operation?
  • Unload a dishwasher and put everything away?
  • Translate spoken Chinese into spoken English
    in real time?
  • Write an intentionally funny story?

Slide from Dan Klein
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
  • Proverb solves crossword puzzles better than most

State of the Art
  • NASA's on-board autonomous planning program
    controlled the scheduling of operations for a
  • Sojourner, Spirit, and Opportunity explore Mars.
  • NASA Remote Agent in Deep Space I probe explores
    solar system.
  • DARPA grand challenge Autonomous vehicle
    navigates across desert and then urban
  • iRobot Roomba automated vacuum cleaner, and
    PackBot used in Afghanistan and Iraq wars.
  • Spam filters using machine learning.
  • Question answering systems automatically answer
    factoid questions.
  • Usable machine translation thru Google.

Recent Times
  • General focus on learning and training methods to
    address knowledge-acquisition bottleneck.
  • Shift of focus from rule-based and logical
    methods to probabilistic and statistical methods
    (e.g. Bayes nets, Hidden Markov Models).
  • Increased interest in particular tasks and
  • Data mining
  • Machine Learning
  • Intelligent agents and Internet
    applications(softbots, believable agents,
    intelligent information access)
  • Scheduling/con?guration applications

Fields of AI
  • Machine Learning
  • Computer Vision
  • Speech Recognition
  • Natural Language Processing
  • Data Mining
  • Information Retrieval
  • Game programming
  • Robotics, Planning

About this course
  • Discussion of recent research problems
  • Theory and Applications
  • Hands-on Experience

About this course Resources
  • Course Book
  • Artificial Intelligence A Modern Approach,
    3/E Stuart Russell Peter Norvig
  • ISBN-10 0136042597
  • ISBN-13  9780136042594
  • Publisher  Prentice Hall Format  Cloth 1152
    pp Published  12/01/2009
  • Book on Learning
  • Machine Learning, Tom Mitchell, McGraw Hill,
  • Additional Readings
  • Research papers

Course Contents
  • Search
  • Uninformed, Informed, Constraint Satisfaction
  • Game Playing
  • Learning
  • Supervised Learning, Bayesian Learning
  • Decision Trees, Adaboost
  • Neural Nets
  • Hidden Markov Models
  • Reinforcement Learning
  • Applications and Research Problems on
  • Computer Vision
  • Natural Language Processing

  • Paper presentation 20
  • Presentation 15
  • Peer review 5
  • Participation 10
  • Participation to presentations
  • Project 35
  • Intermediate report 10
  • Final report 25
  • Final Exam 35

  • Each student should decide on a research project
    related to a field of AI.
  • Project Deadlines
  • Project proposals due 19.10.2011
  • Intermediate project report due 18.11.2011
  • Final report due 26.12.2011

Reading Assignment
  • Alan Turings Computing Machinery and
    Intelligence (1950)
  • First vision of AI