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Introduction to Artificial Intelligence Tuesday and Thursdays 2:00 to 3:15pm, 1404 Siebel Center Jean Ponce 2065 Beckman Institute 333-8864. TA. Tuna Oezer ( 1121 Siebel

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Introduction to artificial intelligence http www courses cs uiuc edu cs440

Introduction to Artificial Intelligence

Tuesday and Thursdays

2:00 to 3:15pm, 1404 Siebel Center

Jean Ponce

2065 Beckman Institute


Introduction to artificial intelligence www courses cs uiuc

  • Tuna Oezer (

    1121 Siebel

    Office hours: Mon. 2:00-3:00 and Th. 1:00-2:00.

This lecture
This Lecture

  • Introduction to AI

  • Course overview

  • Examples: Agents, robots, and vision

  • Next time: Problem solving and search

What is ai

Fast thinking?

Knowing a lot?

Being able to pass as a smart human?

Being able to reason?

Being able to learn?

Being able to perceive and act upon one’s environment?

Writing poetry?

Passing an AI class?

What is AI?

“AI is the study of ideas that enable computers to be intelligent.” [P. Winston]

So, what is intelligence?

Operational definitions of ai

Thinking Humanly

`”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 & McDermott, 1985]

Operational Definitions of AI?

Acting Humanly

”The study of how to make computers do things at which, at the moment, people are better.”

[Rich& Knight, 1991]

Acting Rationally

“The branch of computer science that is concerned with the automation of intelligent behavior.”

[Luger+Stubblefield, 1993]

Which do we choose?

Thinking humanly cognitive science
Thinking Humanly: Cognitive Science

  • 1960s “cognitive revolution”: Information-processing psychology replaced prevailing orthodoxy of behaviorism.

  • Requires scientific theories of internal activities of the brain:

    • What level of abstraction? ``Knowledge'' or ``circuits''?

    • How to validate? Requires:

      • Predicting and testing behavior of human subjects (top-down);

      • Direct identification from neurological data (bottom-up).

  • Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI.

Acting humanly the turing test
Acting humanly: The Turing test

Turing (1950) ``Computing machinery and intelligence'':

  • “Can machines think?'' or “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.

  • Problem: Turing test is notreproducible, constructive, or amenable to mathematical analysis.

Thinking rationally laws of thought
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.

  • 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?

Acting rationally
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.

Is ai a hard problem
Is AI a hard problem?

The meaning of words and sentences

  • John drove his sister to buy groceries.

  • John drove his sister to commit suicide.

  • John drove his car to commit suicide.

  • John drove his rabbit to buy groceries.

Ai prehistory
AI Prehistory

  • Philosophy

  • Mathematics

  • Psychology

  • Linguistics

  • Neuroscience

  • Control Theory

Ai history
AI History

1943 McCulloch & Pitts: Boolean circuit model of brain

1950 Turing's ``Computing Machinery and Intelligence''

1950s Early AI programs, including Samuel's checkers program,

Newell & Simon's Logic Theorist, Gelernter's Geometry Engine

1956 McCarthy organizes Dartmouth meeting and includes Minsky,

Shannon, Newell, Samuel, Simon

Name ``Artificial Intelligence'' adopted

  • General Problem Solver [Newell, Simon, Shaw @ CMU]

    1958 Creation of the MIT AI Lab by Minsky and McCarthy

  • LISP, [McCarthy], second high level language (MIT AI Memo 1)

    1963 Creation of the Stanford AI Lab by McCarthy

    1965 Robinson's complete algorithm for logical reasoning

    1966-74 AI discovers computational complexity …

    1966-72 Shakey, SRI’s Mobile Robot [Fikes, Nilson]

Ai history cont
AI History (Cont.)

1969 Publication of “Perceptrons” [Minsky & Papert],

Neural network research almost disappears

1969-79 Early development of knowledge-based systems

  • SHRDLU, Winograd’s natural language system

    1971 MACSYMA, an symbolic algebraic manipulation system

    1980-88 Expert systems industry booms

  • Japan: Fifth generation project

    US: Microelectronics and Computer Technology Corp.

    UK: Alvey

    1988-93 Expert systems industry busts: ``AI Winter''

    1985-95 Neural networks return to popularity

    1988- Resurgence of probabilistic and decision-theoretic methods

    Computational learning theory

    ``Nouvelle AI'': ALife, GAs, soft computing

Ai research and its spinoffs
AI research and its spinoffs

AAAI Conference

IJCAI Conference

AI Journal


  • Robotics (ICRA, ISRR, IROS)

  • Computer Vision (ICCV, CVPR, ECCV)

  • Neural Networks (NIPS, …)

  • Machine Learning (MLS, …)

  • Speech

  • Natural language understanding

Introduction to artificial intelligence www courses cs uiuc


Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach” (2nd edition), Prentice-Hall, 2003.


  • Problem Sets and MPs (in C): 45%

  • Midterm: 20%

  • Final Exam: 35%


Graduate students
Graduate students

  • Will be graded on a different curve than undergraduates. They are expected to do better for the same grade.

  • May receive ¾ or 1 unit of credit. To receive 1 unit they will have to do a programming project, and should contact my before the Spring break so we can pick a topic.


Introduction (Ch. 1)

Problem solving and search (Ch. 3)

Informed search methods (Ch. 4)

Logical agents (Ch. 7)

First-order logic and inference (Ch. 8 and 9)

Resolution and planning (Ch. 9 and 11)

Uncertainty (Ch. 13)

Bayesian networks (Ch. 14)

Learning (Ch. 18 and 19)

Neural networks (Ch. 20)

Vision and robotics (Ch. 24 and 25)


  • Cheating: You are expected to do all of the work on your own. You may discuss concepts with your classmates, but the homeworks must be done on your own. The penalty for cheating on any assignment is straightforward. On the first occurrence, you will receive a zero for the assignment, and then our course grade will be reduced by one full letter grade. A second occurrence will result in course failure.

  • Late homework: Unless announced in advance, solutions will be posted no sooner than two days after the due date. Homework will be accepted until that point with a penalty of 10% per day that it is late. No assignments will be accepted after the solutions have been posted. To time stamp them, late homeworks will only be accepted in class, during office hours, or electronically by email to the TA's.

Intelligent agents
Intelligent Agents

Anagent is an entity that perceives and acts.

Abstractly, an agent is a function from percept histories to actions:

For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance.

Designing an agent e g a taxi
Designing an agent: (e.g. a taxi)

  • Percept, Action, Goal, Environment (PAGE)

  • Percepts

    • Video, accelerometers, engine sensors, keyboard, voice, sound, GPS, …

  • Actions

    • Steer, accelerate, brake, horn, speak/display, …

  • Goals

    • Safety, reach destination, maximize profits, obey laws, passenger comfort, …

  • Environment

    • US urban streets, freeways, traffic, pedestrians, weather, customers, …

A more realistic goal programming a robot to move boxes
A more realistic goal: programming a robot to move boxes

  • Percepts

    • Vision, haptics, hearing

  • Actions

    • Walk, grab, lift, drop

  • Goals

    • Stack boxes at destination

  • Environment

    • 1404 Siebel

Honda humanoid robots
Honda Humanoid Robots




Ten years


Honda asimo
Honda Asimo

Sony qrio

Toyota trumpet playing robot
Toyota trumpet playing robot

Introduction to artificial intelligence www courses cs uiuc


Courtesy of

S. Leigh

What is it all for?