EECS 391/491: (Introduction to) Artificial Intelligence

Spring 2008, Prof. Michael Branicky

Case Western Reserve University (CWRU)

General Information

Information Sheet

Lecture Hours: MW 9-10:15am, Bingham 103

Course Instructor: Michael Branicky, mb [æt] case [daat] edu, Glennan 517B, x6430

Instructor's Office Hours: M 3-5pm

Course TA / Office Hours:
Zheng Liu, zxl57 [æt] case [daat] edu / R 11:30am-12:30pm, Olin 505

Required Text

• Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. 2nd Ed. Prentice Hall, 2003. ISBN: 0137903952
  ($115 new, $86.25 used in bookstore)

Detailed Syllabus

Mirror Sites

http://dora.case.edu/msb/AI
http://vorlon.case.edu/~msb11/AI

Announcements

• 08 MAY: Final exams were graded and entered into Blackboard yesterday before noon. Hence, complete grade data is available via Blackboard, with your overall "Weighted Total" in the entire course available.

  The grade distribution for the final exam, the "Weighted Total" for the entire course, and the breakdown of letter grades appears in the Final Exam Post-Mortem.

• 06 MAR: Midterm exam grades were posted to Blackboard and midterm grades were delivered to the registrar; click on the the following liink for Midterm Grade Notes. The exam, solutions, and post-mortem are posted below.
• 17 FEB: For those of you who like to work ahead, I have posted all the Problem Sets until the week after Spring Break below. FAQs will update over time. Also, an information sheet and samples for the Midterm Exam appear.
• 12 JAN: For those of you who don't have the book yet, the following resources will help until you obtain it:
  • R&N, 1.1+ (1st edition)
  • R&N, 1.4
  • R&N, Chapter 1 Problems
  • R&N, 26.1-2
  Note: The links to the latter three files have been disabled

Lectures / Assignments

14 JAN: Lecture #01: Course Mechanics / Topics / Background / History [R&N 1.1,3-4]

16 JAN: Lecture #02: The Turing Test [R&N 26.1-2; Turing's Turing Test Article]

21 JAN: MARTIN LUTHER KING JR. HOLIDAY (No Class)

23 JAN: Lecture #03: Intelligent Agents 1; Problem Set #1 Due [R&N 2.1-4]

28 JAN: Lecture #04: Intelligent Agents 2 [Braitenberg's Vehicles, pp. 1-42]   eHandout

30 JAN: Lecture #05: Solving Problems by Searching; Problem Set #2 Due [3.1-4]   eHandout

04 FEB: Lecture #06: Heuristic Search [3.5-6, 4.1-2]   eHandout   eHandout2

Extra handout on A*

06 FEB: Lecture #07: Local Search; Problem Set #3 Due [4.3 plus box on Page 120]   eHandout   eHandout2

11 FEB: Lecture #08: Constraint Satisfaction Problems [5.1,2(but only through "forward Checking", p. 144),3]   eHandout

13 FEB: Lecture #09: Adversarial Search 1 [6.1-3]; Problem Set #4 Due   eHandout

18 FEB: Lecture #10: Adversarial Search 2 [6.4-7]   eHandout   eHandout2

20 FEB: Lecture #11: Predicate Logic and Knowledge Representation; Problem Set #5 Due [7.1-4]

25 FEB: Lecture #12: Reasoning and Logical Agents [7.5-7]   eHandout

27 FEB: Lecture #13: STRIPS Planning; Problem Set #6 Due [11.1,2,4]   eHandout   Sussman Anomaly   (also bring page 2 of previous lecture's eHandout)

03 MAR: Lecture #14: Planning and Acting in the Real World [12.1 (skim), 3, and 7] / Midterm Review; Mini PS #1 Due eHandout   eHandout2   (also bring the Midterm Exam Information Sheet)

05 MAR: Lecture #15: MIDTERM EXAM

10 MAR: SPRING BREAK (No Class)

12 MAR: SPRING BREAK (No Class)

17 MAR: Lecture #16: Uncertainty [13]

19 MAR: Lecture #17: Probabilistic Reasoning 1; Problem Set #7 Due [14.1-4]

24 MAR: Lecture #18: Probabilistic Reasoning 2 [14.5, 7]   eHandout

26 MAR: Lecture #19: Learning 1; Problem Set #8 Due [18.1-3]   eHandouts/Slides

31 MAR: Lecture #20: Learning 2 [18.4-5, 19.1]   eHandouts/Slides   Decision Stump

02 APR: Lecture #21: Statistical Learning Methods 1; Problem Set #9 Due [20.1-3]   eHandout   EM Example

07 APR: Lecture #22: Statistical Learning Methods 2 [20.4-7]   eHandout   RBF Fitting Example

09 APR: Lecture #23: Making Simple Decisions; Problem Set #10 Due [16.1-3, 4 (only through the end of Dominance), 5]   eHandout

14 APR: Lecture #24: Making Complex Decisions [17.1,2 (skip subsection on Convergence), 3]   eHandout

16 APR: Lecture #25: Reinforcement Learning 1; Problem Set #11 Due [21.1-3]   eHandout

21 APR: Lecture #26: Reinforcement Learning 2 [21.4-5]   eHandout

Note: No office hours today (travelling out of town)

23 APR: Lecture #27: Game Theory (Guest Lecturer: Prof. Mehmet Koyuturk); Problem Set #12 Due [17.6,7]   eHandout

28 APR: Lecture #28: Final Review / Cleanup; Mini PS #2 Due   Bring the following:

Final Exam Information Sheet
Midterm Exam Information Sheet
Review Problems (25 pages; we'll do the ones marked with arrows)
Review Problem Worksheet

Solutions/Notes for arrowed problems on Example Final
Scan of Search Problem Solved with Different Algorithms

06 MAY: FINAL EXAM (8:30-11:30AM)

Code Repository

Problem Sets, Mini PSs, and Solutions

[Note: GX.Y problems are for 491 students only.]

Problem Set #01 (due 23 JAN)   FAQ   Solutions   Post-Mortem

Problem Set #02 (due 30 JAN)   FAQ   Solutions   Post-Mortem

Problem Set #03 (due 06 FEB)   FAQ   Solutions   Post-Mortem   491 Model   Errata

Problem Set #04 (due 13 FEB)   FAQ   Solutions   Post-Mortem

Problem Set #05 (due 20 FEB)   FAQ   Solutions   Post-Mortem   491 Model

Problem Set #06 (due 27 FEB)   FAQ   Solutions   Post-Mortem

Mini ProbSet #1 (due MON 03 MAR)   FAQ   Solutions   Post-Mortem

Problem Set #07 (due 19 MAR)   FAQ   Solutions   Post-Mortem

Problem Set #08 (due 26 MAR)   FAQ   Solutions   Post-Mortem   491 Model

Problem Set #09 (due 02 APR)   FAQ   Solutions   Post-Mortem

Problem Set #10 (due 09 APR)   FAQ   Solutions   Post-Mortem

Problem Set #11 (due 16 APR)   FAQ   Solutions   Post-Mortem

Problem Set #12 (due 23 APR)   FAQ   Solutions   Post-Mortem

Mini PS #02 (due MON 28 APR)   FAQ   Solutions   Post-Mortem

Turn in any late Mini Problem Sets to Prof. Branicky's
office before 12 noon on Tuesday, April 29 (75% credit)

Midterm Exam (05 MAR, in class)

Information Sheet

Spring 2005 Solutions/Model

Spring 2007 Exam   Solutions   Model   Post-Mortem   Example 491 Extra Question

Spring 2008 Exam   Solutions   Post-Mortem

Note on Midterm Grades

Midterm Exam Do-Overs

Final Exam (TUESDAY, 06 MAY, 8:30-11:30AM, in class)

Information Sheet

Practice Problems (Note: not all are applicable to us):

• For the last lecture, we'll go over the arrowed ones posted under Lecture #28 above; notes/solutions appear for those we did not have time for; you can do the others that are checked in that eHandout
• You can also visit CMU's AI Course and scroll down for more example Midterm and Final Exams
• For yet more practice, here are some old exams from MIT's AI Course

Post-Mortem

Survey

Survey

Survey Results:   2008   |   2007   |   2005

491 Final Projects

Information Sheet  

Useful Links

Course Resources

Book Websites: AI: The Modern Approach, 2e | Intro to AI: The Modern Approach, 1e

CMU's AI Course

MIT's AI Course

State of the Art

AI in the News: Kasparov vs. Deep Junior [Courtesy: Brandon Rutter]

DARPA Urban Challenge

Other DARPA Programs

CMU's Learning Locomotion Site

USC's Humanoid Robotics Site

University of Alberta's Computer Poker Research Group

The Turing Test

Turing's Turing Test article (in html)

Caltech's Turing Tournament [Courtesy: Andy Horchler]

The Alan Turing Internet Scrapbook

An AI chat bot, ALICE

Jabberwacky

CAPTCHAs: Official Site | Wikipedia

Amusing Turing Test link [Courtesy: Chris Stafford]

In the news: Brain-Robot Interfaces | Is the Turing Test for dogs next?

A Spectrum of Rational Agents

Photopopper Robot

iRobot Homepage

LAAS' museum guide robot Rackham: description and poster

Team Case's DEXTER

Vehicles

A nice Braitenberg Vehicles page and some Example Runs

IBM's Robocode project for programming robotic battletanks in Java

Other neat open-source multi-agent software: Breve, OpenSteer, PlayerStage/Gazebo, Repast, Swarm

Brian Mirtich's Creature War as described in his PhD Thesis (pp. 202-7).

Hebbian Learning, Hebbian Theory, Hebb Rule

Discrete Planning

Steve LaValle's Planning Algorithm book

Steve LaValle's Discrete Planning Chapter his book Planning Algorithms

Chapter 2: Discrete Search Background from Stuart Morgan's MS Thesis

Maze Design [Courtesy Barbara Joy Jones]

Local Search

Karl Sims' Evolving Virtual Creatures: SIGGRAPH paper,   Video

Special AI Talk!

Douglas Lenat Talk (T 05 FEB 2008, 11:30am-12:30pm, White 411)

CSPs

Split Decisions Crossword Puzzles

PROVERB Crossword Puzzle Solver,   Research Papers

Adversarial Search

Checkers is solved

The Perl Journal's Stones Contest: The Perl Journal's Stones Contest

Samuel's Checkers Papers: Part I (Learning) Part II (Alpha-Beta)

A Game Tree Search Visualizer for Chess: Thinking Machine 4 [Courtesy: Michael Rice and Adam Patrick]

Knowledge Representation

Doug Lenat's Cyc | Computers vs. Common Sense (Google Video)

WordNet

OWL: Web Ontology Language

Minesweeper

Uncertainty

Ken Goldberg's Fences Animation

Srinivas Akella's Pachinko machine: Click here then scroll down to "Manipulating Parts with an Array of Pins: A Method and a Machine"

Battleship (game)

Probability

Dice Probability Basics

Probability Primer

Bayes Reasoning Explained

Cool Link

Boston Dynamics' Big Dog [Thanks to Eric Bram]

Function Fitting

Lagrange Interpolating Polynomial

Least Squares Fitting

Least Squares Fitting of Polynomials

Polynomial Fitting Example

Fitting AIDs Data

Decision Trees

UCI Machine Learning Repository

Congressional Voting Data from UCI ML Repository: Full Data Full Explanation

Binary Entropy Table

Tom Mitchell's Machine Learning Book Website

Instance-Based Learning

Case MS Thesis that used Nearest Neighbor and Locally-Weighted Regression

Pandora [Courtesy Barbara Joy Jones]

Decision Theory

2002 Nobel Prize: Prospect Theory and Decision Theory

2002 Nobel Prizes: Citations

Computational Tools for Belief/Decision Networks

Reinforcement Learning

Sutton and Barto's Reinforcement Learning Online

DARPA's Learning Locomotion Program

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Created: 2008-01-12. Last Modified: 2008-05-08. © Michael S. Branicky