CompSci (CS) 171 — Introduction to Artificial
Intelligence — Winter 2012
Current Announcements:
·
(23
March) The Final Exam answer key is posted below and is also available here.
·
(15
March) The quiz #4 answer key is posted below and is also available here.
·
(13
March) *Please* fill out your student course evaluations for
CS-171.
Student course evaluations are very important to me for monitoring and
improving the course content, and very important to UCI for evaluating our
success at our educational mission.
**** Every student who fills out a course
evaluation for CS-171 will receive a bonus of 1% added to their final grade,
free and clear, off the curve, simply a bonus. ****
EEE will return to me the names of students who fill out evaluations (but not
the content, which remains anonymous), provided that enough students fill out
evaluations so that anonymity is not compromised. I will add 1% free
bonus to the final grade of each such named student.
·
(6
March) The quiz #3 answer key is posted below and is also available here.
·
(28
Feb) As announced in class, Chapter 9 (Inference in FOL) has been cancelled in
favor of learning FOL itself better (Chapter 8).
·
(28
Feb) As announced in class and posted to the class email list, the Mid-term
exam is now a pedagogical device.
o
You
can recover 50% of your missed points by showing that you have debugged and
repaired your knowledge base.
o
For
each item where points were deducted, write 2-4 sentences, and perhaps an
equation or two. Describe:
o
*
What was the bug in the knowledge base leading to the error?
o
*
How has the knowledge base been repaired so that the error will not happen
again?
o
Turn
in, with your exam, in class on Thursday, 1 March.
o 50% of your missed points will be forgiven for each correct repair.
· (16 Feb) The Mid-term Exam key has been posted --- click here or see below.
· (31 Jan) The project grading rubric has been posted --- click here or see below.
· (24 Jan) The answer key to Quiz #1, and the project shells, have been posted --- see below.
· Current announcements will appear here, at top-level, for quick and easy inspection.
Place:
ICS-180 (In the
basement of Building 302
on the UCI campus
map)
Time: Tuesday/Thursday 12:30-1:50pm
Instructor: Richard Lathrop
Office hours: Tuesdays 2:30-3:30pm, or anytime by appointment, in DBH-4224
Email: rickl@uci.edu
(If you send email, please
put “CS-171” somewhere in the Subject line.)
TA: Andrew Gelfand
Office hours: Wednesdays 11:00am-noon, or anytime by appointment, in DBH-4099
Email: agelfand@uci.edu
(If you send email, please
put “CS-171” somewhere in the Subject line.)
Goal:
The goal of this class is to familiarize you with the basic
principles of artificial intelligence. You will learn some
basic AI techniques, the problems for which they are applicable, and
their limitations.
The
course content is organized roughly around what are often considered to be
three central pillars of AI: Search, Logic, and Learning. Topics covered
include basic search, heuristic search, game search, constraint satisfaction,
knowledge representation, logic and inference, probabilistic modeling, and
machine learning algorithms.
Class Setup:
The
course will be primarily lecture-based.
There will be a Mid-term and a Final Exam. On every second Tuesday, the first 20
minutes will be an in-class pop quiz, followed by lecture. The frequent quizzes are intended to
encourage you to stay current with the course material. All exams and quizzes may cover all
material presented in class, including lectures and assigned textbook
reading. Quizzes will cover mostly
material presented since the last quiz, and also may include questions that
many students missed on the previous quiz.
The Final Exam will cover mostly material since the Mid-term Exam, and
also will include some questions intended to encourage you to remember the
earlier material.
There
will be an AI coding project, in which you will be provided with a
“dumb” GUI shell, for which you will be required to code the
“smarts.” You are
allowed (but not required) to form teams of two students, following the “Pair Programming”
paradigm. Please note that you are
encouraged to discuss concepts, methods, algorithms, etc.; but you are
forbidden to copy: (1) source code from any source, or (2) text from any source
unless properly cited and set off as a quote. Except for class materials provided from
this class website, you must invent and write all of your own code by yourself
with your partner. Except for
properly referenced material, you must write all of your project report by
yourself with your partner. Please note that your source code and project
report are subject to analysis by automated plagiarism detection programs, and
that direct copying will be treated as an act of academic dishonesty (please see
the section on “Academic Honesty”
below).
Homework
will be assigned, but is not graded. The reason is that prior student course
evaluations alerted me to the existence of student cheating by way of copying
the homework answers. I deplore
this degree of personal degradation in dishonest students, but I cannot control
it, and so I avoid the opportunity.
I remain determined to create a fair and honest educational experience
for all students, as best I can.
Textbook
Required:
Russell & Norvig : Artificial Intelligence; A Modern Approach, 3rd
edition.
The
course is based on, and the UCI bookstore has, the 3rd edition. The
assigned textbook reading is required, and is fair game for quizzes and
exams. You place yourself at a
distinct disadvantage if you do not have the textbook.
I
strongly recommend the 3rd edition. R&N estimates that about 20% of the
material in the 3rd edition is new from the 2nd
edition. Several of the chapters
and exercises have been rearranged. (A kind and helpful student has
elucidated some of the differences between the 2nd & 3rd
editions; click here.)
Also,
for your convenience, I have requested that a copy of the textbook be placed on
reserve in the UCI Science Library. There is a two-hour check-out limit.
I do deplore
the high cost of textbooks. You are
likely to find the book cheaper if you search online at EBay.com, Amazon.com,
and related sites.
A student
kindly contributed the following suggestion, for which I cannot vouch, and
which I provide for your use if it is useful to you:
Hello,
I just wanted to point out that there does exist an
international edition of the book which can be bought for around $40-50. I
cannot comment on what specific differences there are for this particular book,
though they are usually very small (exercises moved around, etc).
Obviously, it is in paperback.
http://www.valorebooks.com/affiliate/buy/siteID=e79mzf/ISBN=0136042597
http://www.abebooks.com/servlet/BookDetailsPL?bi=4161131466&cm_ven=sws&cm_cat=sws&cm_pla=sws&cm_ite=4161131466&afn_sr=para¶_l=1
http://www.biblio.com/books/360025589.html
Personally I plan on using this book for a while so I bought the hardcover
version, but I just wanted to point out that this is an option for those
looking for a more 'economical' route.
~ XXXXXX [name anonymized to protect student privacy]
Grading:
Your
grade will be based on the bi-weekly quizzes
(20%), a project (20%), a mid-term exam (25%), and a final exam (35%). Homework is assigned but ungraded.
·
Quizzes will be given the first 20 minutes of class every
second Tuesday (starting the third Tuesday, and adjusted for the mid-term
exam). Your lowest quiz score will
be discarded in computing your grade.
It is not possible to make-up missed quizzes, but one missed quiz may be
discarded as your lowest quiz score.
·
The mid-term exam will be given in class on Thursday,
February 16, and is closed-book, closed-notes.
·
The final exam will be given on Friday, March 23, 10:30 a.m.
- 12:30 p.m., and is closed-book, closed-notes. The final exam will cover all course
material from the entire quarter, but mostly the second half.
Dates
and times for all final exams are set by the UCI Registrar (Final Exam
Schedule 2011-12).
·
Every student who fills out a course evaluation
for CS-171 will receive a bonus of 1% added to their final grade, free and
clear, off the curve, simply a bonus.
EEE will return to me the names of students who fill out evaluations (but not the content, which remains anonymous), provided that enough students fill out evaluations so that anonymity is not compromised. I will add 1% free bonus to the final grade of each such named student.
Student
course evaluations are very important to me for monitoring and improving the
course content, and very important to UCI for evaluating our success at our
educational mission. *Please* fill
out your student course evaluations.
·
“Bonus Points” will be awarded, at my sole
discretion, (1) to the first student who spots a genuine technical error (typos
don’t count) in any of the course materials before I spot it too, and (2)
for helpful contributions to the class as we go along. One bonus point is equivalent to one
quiz point.
Syllabus:
The following represents a preliminary syllabus. Some changes in the
lecture sequence may occur due to earthquakes, fires, floods, wars, natural
disasters, unnatural disasters, or the discretion of the instructor based on
class progress.
Background Reading and Lecture Slides will be changed or revised as the
class progresses at the discretion of the instructor. Please note: I may tweak or revise the lecture slides
prior to the lecture; please ensure that you have the current version.
Please read the assigned textbook reading in
advance of each lecture, then again after each
lecture.
Week 1:
Tue., 10 Jan., Introduction, Agents.
Read
in Advance: Textbook Chapters 1-2.
Lecture
slides: Introduction, Agents [PDF; PPT].
Optional
URL:
Association for the
Advancement of Artificial Intelligence (AAAI)
AAAI’s
digital library of more than 10,000 AI technical papers
AAAI’s
Author Kit
Optional Cultural Interest:
IBM Watson: Final Jeopardy! and the Future of Watson
AI vs. AI.
Two chatbots talking to each other.
Thu., 12 Jan., Uninformed Search.
Read
in Advance: Textbook Chapter 3.1-3.4.
Lecture
slides (two parts):
(1)
Introduction to Search [PDF; PPT]; and
(2)
Uninformed Search [PDF; PPT].
Optional
Reading:
John
McCarthy, “What
Is Artificial Intelligence?”
HTML
and other versions of “What is AI?”
Optional
URL:
John McCarthy Homepage
Optional Cultural Interest:
Boston Dynamics Big Dog (new
video March 2008)
Week 2:
Tue., 17 Jan., Heuristic Search.
Read
in advance: Textbook Chapter
3.5-3.7.
Lecture
slides: Heuristic Search [PDF; PPT].
Optional
Cultural Interest:
Infinite Mario AI - Long
Level
An attempt at a Mario AI using the A* path-finding algorithm.
It
claims the bot won both Mario AI competitions in 2009.
See
also http://www.marioai.org/.
Thu., 19 Jan., Local Search.
Read in advance: Textbook Chapter 4.1-4.2.
Lecture
slides: Local Search [PDF; PPT].
Optional
Lecture Slides:
Optional
Reading:
Autonomous car - Wikipedia,
the free encyclopedia
“Autonomous
Driving in Traffic: Boss and the Urban Challenge” (2009).
“Stanley: The
Robot that Won the DARPA Grand Challenge” (2005).
Optional Cultural Interest:
Google Car: It Drives Itself
- ABC News
[Part 1/3] The Evolution of
Self-Driving Vehicles
[Part 2/3] How Google's
Self-Driving Car Works
[Part 3/3] Google's
Self-Driving Golf Carts
DARPA Urban Challenge
Highlights
Princeton
DARPA Grand Challenge - Crash Video
DARPA Urban Challenge: Ga Tech hits curb
DARPA Urban Challenge - Sting
Racing crash
[DARPA] Team Oshkosh attempts
forced Entry to Main Exchange
[DARPA] Alice's Crash
(spectator view)
[DARPA] Alice's Crash
(road-finding camera) [different view of above; long]
DARPA Urban Challenge Crash
Cornell MIT
DARPA Urban Challenge - robot
car wreck [different view of above]
Optional Ungraded Homework:
Week 3:
Tue.,
24 Jan., Quiz #1
(answer key here);
start Games/Adversarial Search.
Read in advance: Textbook
Chapter 5.1-5.5.
Lecture
slides: Games/Adversarial Search [PDF; PPT].
Optional
Reading:
Minton,
et. al., 1990, AAAI "Classic
Paper" Award recipient in 2008.
How to solve the 1 Million Queens problem and schedule the Hubble
telescope.
Optional Cultural Interest:
Thu., 26 Jan., finish Games/Adversarial Search.
Read in advance: Textbook
Chapter 5.1-5.5.
Lecture
slides: Games/Adversarial Search (above).
Optional Reading:
Newell & Simon’s “Symbols and Search” Turing
Award Lecture (1976).
Herbert
Simon was awarded a Nobel Prize (in economics, 1978).
Optional URL:
The
program learns to build a car using a genetic algorithm.
Optional Cultural Interest:
Arthur
C. Clarke “Quarantine.”
A science fiction short story by a classic master, in 188 words.
Optional Ungraded Homework:
Week 4:
Tue., 31 Jan., start Constraint Satisfaction.
Read in advance: Textbook
Chapter 6.1-6.4, except 6.3.3.
Lecture
slides: Constraint Satisfaction Problems [PDF;
PPT].
Optional
Reading:
Campbell, et al., 2002, Artificial
Intelligence, “Deep Blue.” [PDF]
(URL
http://www.sciencedirect.com/science/article/pii/S0004370201001291)
Thu., 2 Feb.,
finish Constraint Satisfaction.
Read in advance: Textbook
Chapter 6.1-6.4, except 6.3.3.
Lecture
slides: Constraint Satisfaction Problems (above).
Optional Reading: Chaslot, et al.,
“Monte-Carlo
Tree Search: A New Framework for Game AI,”
in Proceedings
of the Fourth Artificial Intelligence and Interactive Digital Entertainment
Conference,
AAAI Press, Menlo Park, pp. 216-217, 2008.
An interesting combination of Local Search (Chapter 4) and Game
Search (Chapter 5).
Optional URL: “Everything
Monte Carlo Tree Search” website.
Optional Ungraded Homework:
Week 5:
Tue.,
7 Feb., Quiz
#2 (answer key here);
start Propositional Logic.
Read in advance: Textbook
Chapter 7.1-7.4.
Lecture
slides: Propositional Logic A [PDF; PPT].
Thu., 9 Feb., finish Propositional Logic.
Read in advance: Textbook Chapter 7.5-7.8.
Lecture slides:
Propositional Logic B [PDF; PPT].
Optional Reading:
Alan
Turing’s classic paper on AI (1950).
(URL
http://www.loebner.net/Prizef/TuringArticle.html)
Alan
Turing is the most famous computer scientist of all time.
The Turing Award is the
highest honor in computer science.
The Turing Machine is
still our fundamental theoretical model of computation.
Turing’s work on the
Enigma code in WWII led to programmable computers.
AAAI/AI
Topics: The Turing Test:
“Can Machines Think?”
Wikipedia
“Computing
Machinery and Intelligence”
No
homework --- study for the Mid-term Exam.
Week 6:
Tue.,
14 Feb., Catch-up,
Review for Mid-term Exam.
Read in advance: Textbook
Chapters 1-7 (only sections assigned above).
Lecture
slides: Catch-up, Review, Question&Answer
[PDF;
PPT].
Thu., 16 Feb., Mid-term Exam (answer key here).
Read in advance: Textbook
Chapters 1-7 (only sections assigned above).
Lecture
slides: Catch-up, Review, Question&Answer
(above).
Optional Ungraded Homework:
Week 7:
Tue.,
21 Feb., Review Mid-term Exam; start First Order Logic
Read in advance: Textbook
Chapter 8.1-8.2.
Lecture
slides: First Order Logic Syntax [PDF; PPT].
Thu., 23 Feb., finish First Order Logic; Knowledge
Representation.
Read in advance: Textbook
Chapter 8.3-8.5.
Lecture slides (two parts):
(1) First Order Logic Semantics [PDF; PPT];
and
(2) First Order Logic
Knowledge Representation [PDF;
PPT].
Optional
Reading:
Ferrucci, et al., 2010, “Building Watson: An Overview of the DeepQA Project”
(URL
http://www.stanford.edu/class/cs124/AIMagazine-DeepQA.pdf)
Optional Ungraded Homework:
Week 8:
Tue., 28 Feb., Quiz #3 (answer key here);
Inference in First Order Logic.
Read in advance: Textbook Chapter 9.1-9.2, 9.5.1-9.5.5.
Lecture
slides: First Order Logic Inference [PDF; PPT].
Thu., 1 Mar., Probability, Uncertainty, Bayesian Networks. Lecture by Andrew Gelfand.
Read in advance: Textbook
Chapters 13, 14.1-14.2.
Lecture slides: Reasoning Under Uncertainty [PDF; PPT].
Fri., 2 Mar., Project Part 1 due: Uniformed Searches (Breadth-First, Depth-First, Uniform Cost, Bidirectional [using Uniform-Cost], Iterative Deepening)
Optional
Reading:
Searching for Commonsense: Populating Cyc from the Web, Matuszek et al, AAAI 2005
(URL
http://www.cyc.com/doc/white_papers/AAAI051MatuszekC.pdf)
Optional Reading: An Introduction to the Syntax and Content of Cyc, Matuszek et al, AAAI Spring Symposium, 2006
Optional Ungraded Homework:
Week 9:
Tue., 6 Mar., start Learning from Examples.
Read in advance: Textbook
Chapter 18.1-18.4.
Lecture slides: Intro to Machine Learning [PDF; PPT].
Thu., 8 Mar., finish Learning from Examples; Probabilistic
Learning.
Read in advance: Textbook
Chapter 18.5-18.12, 20.1-20.3.2.
Lecture slides:
Learning Classifiers [PDF;
PPT].
Viola & Jones, Learning, Boosting, Vision [PDF; PPT] (read the paper immediately below!)
Optional
Reading: Viola
& Jones, 2004, “Robust Real-Time Face Detection”
Optional
Reading: Freund
& Schapire, 1999, “A Short Introduction to
Boosting”
Optional Reading: Danziger, et al., 2009, “Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning”
Week 10:
Tue., 13 Mar., Quiz
#4 (answer key here);
Special Topics Lecture.
Lecture slides: “Intelligent Systems and Molecular Biology” [PPT].
Thu., 15 Mar., Catch-up, Review for Final Exam.
Read in advance: Textbook,
review all assigned reading.
Lecture
slides: Review, Catch-up, Question&Answer [PDF; PPT].
Fri., 16 Mar., Project Part 2 due: Heuristic Searches (Greedy Best First, A*)
Final Exam:
Fri., 23 Mar, 10:30am-12:30pm Final Exam (answer key here).
Project:
[Maze Solver].
This project corresponds to Informed Search Problems (Chapter 3 in your
book). Your job is to write an AI agent that can find paths through a maze
better than you can. Click
here for the grading rubric.
You are employed by a company
that makes interactive video games. The game's 2D world has a large number of
fixed polygonal obstacles of various shapes and sizes on the screen. You are
assigned to write the controller for one of the moving figures on the screen.
Whenever a gold coin appears on the screen, your figure is to walk to the coin
as rapidly as possible, avoiding all obstacles. Input is the screen locations
of your figure, the gold coin, and the locations and shapes of all obstacles.
Output is to be the path your figure will follow to the coin. [Click here for
solved examples.]
Interestingly, this is also the
same basic problem as robot arm navigation through a crowded workspace. In the
robot arm navigation problem, the task is to go from one arm position (the
initial position) to some other specified arm position (the goal position)
without colliding with any objects in the workspace. In general, this problem
is representative of a whole class of related planning and navigation problems.
Demonstrate,
benchmark, and compare Breadth-First, Depth-First, Uniform Cost, Bidirectional
(using Uniform-Cost), Iterative Deepening, Greedy Best First, and A* search on
this route-finding problem.
[A
Java shell is available (and zipped); a Java
description is available; a C++
shell is available (and zipped);
a C++
description is available; an example
game is available; test files
are available]
EXTRA
CREDIT OPPORTUNITIES:
5%
will be added to your total project score for each of the following
opportunities:
·
5% for implementing a heuristic
that dominates (and sometimes betters) straight-line distance (must execute and
succeed on “bigMaze”).
·
5% for devising a maze that takes
the “example game” longer to solve with A* than “bigMaze” takes --- using no more total vertices than
“bigMaze” has.
·
5% (to one team only) for devising a maze that takes
the “example game” longer to solve than any other team’s maze
entry --- using no more total vertices than “bigMaze”
has.
More details, and a grading rubric, will be given shortly.
Meanwhile, form teams (no more than two people per team)
and get started!
Study Guides --- Previous CS-171 Quizzes,
Mid-term, and Final exams:
Previous
CS-171 Quizzes, Mid-term exams, and Final exams are available here as study
guides.
As an
incentive to study this material, at least one question from a previous Quiz or
Exam will appear on every new Quiz or Exam. In particular, questions that many
students missed are likely to appear again. If you missed a question, please
study it carefully and learn from your mistake --- so that if it appears again,
you will understand it perfectly.
Winter
Quarter 2012:
Mid-term
Exam and key
Final
Exam and key
Spring
Quarter 2011:
Mid-term
Exam and key
Final
Exam and key
Spring
Quarter 2004:
Spring
Quarter 2000:
Quiz
#1 key
Quiz
#2 key
Quiz
#3 key
Quiz
#4 key
Quiz
#5 key
Final
Exam key
Online Resources:
Additional Online Resources may be posted as the class progresses.
Textbook website for Artificial Intelligence: A Modern Approach
(AIMA).
AIMA page for additional online
resources.
American Association for Artificial
Intelligence (AAAI) website.
AAAI “AI
Topics.”
AAAI “Student Resources.”
AAAI
“Classic Papers.”
AAAI
Annual Conference.
Much of the text for the HAL book .
Well worth
looking at to get a broad and entertaining view of the history of AI.
Academic Honesty:
Academic dishonesty is unacceptable and will not be
tolerated at the University of California, Irvine. It is the responsibility of
each student to be familiar with UCI's current academic honesty policies.
Please take the time to read the current UCI
Senate Academic Honesty Policies and the ICS School
policy on cheating.
The policies in these documents will be adhered to scrupulously. Any student who engages in cheating, forgery, dishonest conduct, plagiarism, or collusion in dishonest activities, will receive an academic evaluation of ``F'' for the entire course, with a letter of explanation to the student's permanent file. The ICS Student Affairs Office will be involved at every step of the process. Dr. Lathrop seeks to create a level playing field for all students.