Bil682 Yapay Anlayış Artificial Intelligence Güz 2011 Dr. Nazlı İkizler Cinbiş



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Bil682 - Yapay Anlayış Artificial Intelligence

  • Güz 2011

  • Dr. Nazlı İkizler Cinbiş

  • Slides mostly adapted from AIMA


What is AI?

  • A system is rational if it does the right thing given what it knows



Acting humanly: Turing Test

  • Alan Turing (1950) "Computing machinery and intelligence":

  • 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 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

  • 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 information
    • Doesn't necessarily involve thinking – e.g., blinking reflex
    • But thinking should be in the service of rational action
    • 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 performance



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, we’re just approximating rationality

    •  design best program for given machine resources and available knowledge


Foundations of 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 activity

  • Psychology phenomena of perception and motor control, experimental techniques

  • Computer Science hardware, algorithms, computational complexity theory, fast computers

  • 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 Intelligence"

  • 1956 Dartmouth meeting: "Artificial Intelligence" adopted

  • 1952—69 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 reasoning

  • 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

  • 1966—73 AI discovers computational complexity Neural network research almost disappears

  • 1969—79 Early development of knowledge-based systems

  • 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).



Knowledge is Power: Expert Systems

  • Discovery that detailed knowledge of the specific 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: Configure computers for DEC (1982)


AI Industry

  • Development of numerous expert systems in early eighties.

  • Estimated $2 billion industry by 1988.

  • Japanese start “Fifth Generation” project in 1981 to build

  • intelligent computers based on Prolog logic programming.

  • MCC established in Austin in 1984 to counter Japanese project.

  • Limitations become apparent, prediction of AI Winter

    • Brittleness and domain specificity
    • Knowledge acquisition bottleneck


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 language.

  • Industrial applications:

    • Character and hand-writing recognition
    • Speech recognition
    • Processing credit card applications
    • Financial prediction
    • Chemical process control


What Can AI Do?

  • Quiz: Which of the following can be done at present?

  •  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?



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 humans



NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft

  • NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft

    • 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 environment.

  • 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 applications

    • Data mining
    • Machine Learning
    • Intelligent agents and Internet applications(softbots, believable agents, intelligent information access)
    • Scheduling/configuration 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, 1997.

  • 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


Evaluation

  • Paper presentation %20

    • Presentation %15
    • Peer review %5
  • Participation %10

    • Participation to presentations
  • Project %35

    • Intermediate report %10
    • Final report %25
  • Final Exam %35



Project

  • 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 Turing’s “Computing Machinery and Intelligence” (1950)

    • First vision of AI




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