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
Detailed Syllabus

Mirror Sites

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

Announcements

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


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