v
Have
a Happy Holidays!! and a Joyous,
Healthy, Prosperous, and Successful New Year!!
v
The
Final Exam key has been posted below, and is also available here.
v
Please,
fill out your student evaluations for CS-171.
****
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. ****
These
evaluations are important to UCI in monitoring our quality and success in
fulfilling our educational mission, and they are important to me in improving
the CS-171 experience.
Knowing
what positive features you found good and strong helps me know what to repeat
and emphasize. Many of the positive
features in the current offering of CS-171 were suggested as improvements by
previous year's students.
Please,
fill out your student evaluations for CS-171.
v
According
to the UCI Registrar quarter calendar website (http://www.reg.uci.edu/calendars/quarterly/2014-2015/quarterly14-15.html),
“Instruction ends” occurred yesterday, Fri., Dec 12. Consequently,
no discussion sections will occur Monday or Tuesday of next week (Dec. 15 or 16).
Instead, please schedule an office hours appointment
with your TA or me by email.
v
The
Quiz #4 answer key has been posted below and also is available here.
v
Please
note that P(A and B) = P(A) + P(B) - P(A or B).
If,
and only if, A and B are independent, then and only then P(A
and B) = P(A)*P(B).
If
A and B are disjoint then P(A and B) = 0.
If
A and B are synonyms (i.e., co-occur exactly) then P(A and B) = P(A) = P(B).
v
ICS
has made a video of the "ICS FACULTY PANEL ON IMPROVING YOUR GRAD SCHOOL
APPLICATION" ?
It
is available on YouTube (and also from the ICS website):
http://www.youtube.com/watch?v=P50PrQ7SOuQ
Students
interested in grad school who were not able to attend the panel in person still
would benefit from watching the video.
v
Sridevi,
the class Reader, kindly has supplied the Quiz #1 break-down of the fraction of
students who got zero, partial, or perfect credit on each question. These
statistics have been prepended to the Quiz #1 key posted below, and also are
available here.
v
The
following seminar will be of interest to students interested in machine
learning:
“Never-Ending
Language Learning” --- the AI/ML seminar in Bren Hall 4011 from 3 to 4pm.
Tom
M. Mitchell
E.
Fredkin University Professor
Machine
Learning Department
Carnegie
Mellon University
ABSTRACT: We will never really understand the process
of learning from experience, until we can build machines that learn many
different things, over years, and become better learners over time.
We
describe our research to build a Never-Ending Language Learner (NELL) that runs
24 hours per day, forever, learning to read the web. Each day NELL extracts (reads) more
facts from the web, into its growing knowledge base of beliefs. Each day NELL also learns to read better
than the day before. NELL has been
running 24 hours/day for over four years now. The result so far is a collection
of 70 million interconnected beliefs (e.g., servedWtih(coffee,
applePie)), NELL is considering at different levels
of confidence, along with millions of learned phrasings, morphological
features, and web page structures that NELL uses to extract beliefs from the
web. NELL is also learning to reason over its extracted knowledge, and to
automatically extend its ontology. Track NELL's progress at
http://rtw.ml.cmu.edu, or follow it on Twitter at @CMUNELL.
BIO: Tom M. Mitchell founded and chairs
the Machine Learning Department at Carnegie Mellon University, where he is the
E. Fredkin University Professor. His research uses machine learning to
develop computers that are learning to read the web, and uses brain imaging to
study how the human brain understands what it reads. Mitchell is a member of the U.S.
National Academy of Engineering, a Fellow of the American Association for the
Advancement of Science (AAAS), and a Fellow and Past President of the
Association for the Advancement of Artificial Intelligence (AAAI). He believes the field of machine
learning will be the fastest growing branch of computer science during the 21st
century.
FOR
THE CURRENT SCHEDULE OF TALKS SEE:
http://cml.ics.uci.edu/?page=events&subPage=aiml
v
The
Quiz #3 key has been posted below, and also is available here.
v
Mid-term
exams will be passed out at the end of class Tues., Nov. 25, for those students
who have not yet gotten them. Otherwise, you may get them from Sridevi, the Reader, during her office hours Wednesday
4:00-5:00pm, or anytime by appointment, in DBH-3221. The deadline for the
Mid-term Pedagogical Device is the beginning of class, Tuesday, 2 Dec. See
instructions on the new page 2 of the Exam key posted below.
v
Dr.
Lathrop’s office hours for Weds., 26 Nov., are
canceled due to a death in the family.
v
As
announced in lecture and on the class mailing list, the Mid-term Exam is now a
designated Pedagogical Device. You
can receive 50% of your missed points back by repairing the bugs in your
knowledge base that led you to miss points. The deadline for the Mid-term Pedagogical
Device has been extended to the beginning of class, Tuesday, 2 Dec. See
instructions on the new page 2 of the Exam key posted below.
v
The
Reader, Sridevi, kindly has provided a break-down of
the fraction of students who got zero, partial, or perfect credit on each
question of the Mid-term Exam and Quiz #2. Quiz #1 will follow. I have inserted this analysis as the new
page 1 of those keys posted below.
v
The
Mid-term Exam key has been posted below, and also is available here.
v
As
a suggestion, please arrive a few minutes early for the Mid-term Exam, spread
out, and take your seat quickly. Doing so will allow us to pass out the exam
quickly and give you the most possible time to work the exam.
v
If
you drew a line across branch arcs instead of crossing out leaf nodes for Quiz
#2, problem #2, your answer will be considered correct if the inferred leaf
nodes to be pruned are correct. Please check your quiz if so, and ask the
Reader about any problems. But,
next time, please cross out the leaf nodes; it is faster and more accurate to
grade.
v
A
kind and helpful student has brought it to my attention that the PDF reader on
a Mac (iPad) sometimes has difficulty reading the previous CS-171 tests
correctly. For example, in Quiz #2 from SQ’2004, problems #3a and #3b,
the erroneous “Y” on the key was corrected by an overlay in the PDF
file of a red X through the Y, and next to it a red N. However, this overlay is
invisible on a Mac (iPad), leading to incorrect understanding of the right
answer. Sometimes the Mac PDF software is incompatible with the PC PDF
software. If you are using a Mac to read the previous CS-171 test PDF files and
something looks wrong, please bring it to my attention and then look at it
again with a PC.
v
A
kind and helpful student has contributed a URL for the “Complete Map of Optimal Tic-Tac-Toe Moves.”
v
A
kind and helpful student has contributed a URL for “Google
reveals it is developing a computer so smart it can program ITSELF.”
v
The
Quiz #2 answer key has been posted below and also is available here.
v
The
Quiz #1 answer key has been posted below and also is available here.
v
Dr.
Lathrop’s office hours for Wednesday, 22 Oct., are canceled due to travel
to Washington, D.C., as Chair of the NIH grant review panel on “Big Data
to Knowledge: Targeted Software.”
v
Several
interested students have asked what AI-related courses beyond CS-171 might be
taught next quarter (Winter’2015). Although the
schedule is still tentative and subject to change and revision, these courses
appear in the current tentative schedule:
COMPSCI 116. Computational Photography and
Vision. (Prof. Ramanan)
COMPSCI 172B. Neural Networks and Deep
Learning. (Prof. Baldi)
COMPSCI 175. Project in Artificial
Intelligence. (Prof. Smyth)
COMPSCI 178. Machine Learning and
Data-Mining. (Prof. Ihler)
v
A
kind and helpful student has contributed a search strategy visualization tool,
which appears below in the Search material of Week 2 under “Interesting
search algorithm visualization web page” and is also available here.
v
Some
students were unclear about the Tabu Search wrapper
and implementation, so I added a slide on that topic to the Local Search
lecture slides.
v
The
lecture of Tue., 25 Nov., Probability, Uncertainty, Bayesian Networks, will be
given by Robert Hasselbeck.
v
Coding
shells for Monster Sudoku (Chapter 6, Constraint Satisfaction) and Maze Path-finding
(Chapter 3, State Space Search) have been posted to the Project section below.
These shells are for students who are interested and want to have fun with the
class material, “just for fun on an optional ungraded basis.” I may
award Bonus Points to students who contribute bug fixes or otherwise improve
the projects.
v
Please
plan to attend the ICS Faculty Panel on
Improving Your Grad School Application, Tuesday, 21 October, noon-12:50pm
in DBH-6011. (If you have a time
conflict, note that the video will be posted on the ICS SAO website.) Please review the US Bureau of Labor
Statistics chart on “Earnings
and unemployment rates by educational attainment.”
v
Office
hours update: The Reader, Sridevi Maharaj,
will hold office hours Wednesday 4:00-5:00pm, or anytime by appointment, in
DBH-3221. Rick Lathrop’s office hours have changed to Wednesday
2:00-3:00pm, or anytime by appointment, in DBH-4224.
v
A
“just for fun on an optional ungraded basis” coding shell for Game
AI (Adversarial search) has been posted below. I hope to get shells for Sudoku
(Constraint satisfaction) and maze path-finding (Heuristic search) posted
shortly. I have no people resources available to fix problems if they come up,
but I will give Bonus Points to motivated students who do so.
v Accommodations for missed quizzes or exams have been extended to include field maneuvers of the US military or National Guard (I require a copy of your official orders).
v The lecture of Thu., 23 Oct., start Constraint Satisfaction, will be given by Mahdi Tehrani. A lecture by Robert Hasselbeck will be scheduled sometime later in the term.
v If you are an Exchange student, or for any other reason are not on the EEE class mailing list, please let me know so that I can make other arrangements for you to get class email.
v Several students have expressed an interest in doing one or more coding projects “just for fun” on an optional ungraded basis, so the coding shells will be posted shortly.
v ROOM
CHANGE!! ELH-100 beginning Tuesday,
Oct. 7.
v Current announcements will appear here, at top-level, for quick and easy inspection.
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 expect that you have a personal copy
of the textbook, and quizzes and exams are written accordingly.
Please
purchase or rent your own personal textbook for the quarter (and then resell it
back to the UCI Bookstore at the end if you don't want it for reference).
Please do not
jeopardize your precious educational experience with the false economy of
trying to save a few dollars by not having a personal copy of the textbook.
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. However,
please understand that with high student enrollments, it is unrealistic to
expect that these thin reserves will always be available when you need
them. Please
purchase or rent your own personal textbook.
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 link(s) to a PDF of the course textbook, for which I
cannot vouch:
http://en.tjcities.com/wp-content/uploads/Books/Artificial_Intelligence_3rd.pdf (possibly stale?)
http://crazy-readers.blogspot.com/2013/08/artificial-intelligence-modern-approach.html
You
can also try to search the Internet for “artificial intelligence a modern
approach pdf 3rd edition”. Several more hits turned up the last time I
did so.
A
student kindly contributed the following suggestion, for which I cannot vouch:
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]
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 and review the lecture notes in advance of each lecture, then again after each lecture.
Thu., 2 Oct.,
Introduction, Agents.
Read
in Advance: Textbook Chapters 1-2.
Lecture
slides: Introduction, Agents [PDF; PPT].
Optional Cultural Interest:
IBM Watson: Final Jeopardy! and the Future of Watson
AI vs. AI.
Two chatbots talking to each other.
Optional
Reading:
John
McCarthy, “What
Is Artificial Intelligence?”
AAAI,
AI Overview.
Tue., 7 Oct., Uninformed Search.
Read
in Advance: Textbook Chapter 3.1-3.4.
Lecture
slides (three parts):
(1)
Introduction to Search [PDF; PPT]; and
(2)
Uninformed Search [PDF; PPT].
Optional Cultural Interest:
Boston Dynamics Big Dog (new video
March 2008)
Cheetah Robot runs 28.3 mph;
a bit faster than Usain Bolt
Optional Reading:
Newell & Simon’s “Symbols and Search” Turing
Award Lecture (1976).
Herbert
Simon was awarded a Nobel Prize (in economics, 1978).
Thu., 9 Oct.,
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.
“You
can see the path it plans to go as a red line, which updates when it detects
new obstacles at the right screen border. It uses only information visible on
screen.”
See
also http://www.marioai.org/.
Interesting
search algorithm visualization web page.
Optional Cultural Interest:
A* Search in Interplanetary Trajectory Design, courtesy of Eric Trumbauer, former
CS-271 student.
Eric
comments, “One thing to possibly discuss with the last slide is that the
itinerary it settles on does stay at a higher energy for a little bit until it
passes closest to Europa, maximizing the velocity before the insertion sequence
to the lower energy. This is indeed
optimal behavior, as opposed to immediately reducing its energy as a Greedy
Best First algorithm using this heuristic would want to do.”
A*
Search in Protein Structure Prediction, Lathrop and Smith, J. Mol. Biol.
255(1996)641-665
Optional Reading:
Alan Turing’s classic paper on AI (1950).
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”
Tue., 14 Oct., Local Search.
Read in advance: Textbook Chapter 4.1-4.2.
Lecture
slides (two parts):
(1)
Local Search [PDF;
PPT]; and
(2)
Representation [PDF; PPT].
Optional URLs:
“Hill
Climbing with Simulated Annealing”
The
program learns to build a car using a genetic algorithm
Optional
Reading:
Minton,
et. al., 1990, AAAI "Classic
Paper" Award recipient in 2008.
How to solve the 1 Million Queens problem and schedule space
telescopes.
Optional
Lecture Slides:
Optional Ungraded Homework:
Thu., 16 Oct., 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
Cultural Interest:
RoboCup 2012 Standard Platform: USA / Germany (Final).
Optional URL: “Complete Map of Optimal Tic-Tac-Toe Moves.”
Optional
Reading:
Campbell, et al., 2002, Artificial
Intelligence, “Deep Blue.” [PDF]
(URL
http://www.sciencedirect.com/science/article/pii/S0004370201001291)
Details about the AI system that beat the human chess champion.
Tue.,
21 Oct., finish Games/Adversarial Search.
Read in advance: Textbook
Chapter 5.1-5.5.
Lecture
slides: Games/Adversarial Search (above).
Optional Cultural Interest:
Arthur
C. Clarke “Quarantine.”
A science fiction short story written by a classic master, in 188
words.
He
was challenged to write a science fiction short story that would fit on a
postcard.
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:
Thu., 23 Oct., start Constraint Satisfaction.
(Lecture by
Mahdi Tehrani.)
Read in advance: Textbook
Chapter 6.1-6.4, except 6.3.3.
Lecture
slides: Constraint Satisfaction Problems [PDF;
PPT].
Optional Cultural Interest:
Tue.,
28 Oct., finish Constraint Satisfaction.
Read
in advance: Textbook Chapter 6.1-6.4, except 6.3.3.
Lecture
slides: Constraint Satisfaction Problems [PDF;
PPT].
Optional Cultural Interest:
Tesla Model S P85D AWD and
auto-pilot demo
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
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
Reading:
Autonomous car - Wikipedia,
the free encyclopedia
“Autonomous
Driving in Traffic: Boss and the Urban Challenge” (2009).
Thu., 30 Oct., Quiz #2 (answer key here);
start Propositional Logic.
Read
in advance: Textbook Chapter 7.1-7.4.
Lecture slides: Propositional Logic A [PDF; PPT].
Optional Cultural Interest (Happy Halloween! snakes,
spiders, and a talking head!):
“Asterisk - Omni-directional
Insect Robot Picks Up Prey #DigInfo”
“Freaky AI robot, taken from Nova science now”
Optional
Ungraded Homework:
Tue.,
4 Nov., finish Propositional Logic.
Read
in advance: Textbook Chapter 7.5 (optional: 7.6-7.8).
Lecture slides: Propositional Logic B [PDF; PPT].
Additional
Discussion lecture slides [PDF].
Optional
Cultural Interest:
“Janken
(rock-paper-scissors) Robot with 100% winning rate”
Thu., 6 Nov., 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].
Optional
Cultural Interest:
“Quadrocopter Pole Acrobatics”
“Nano Quadcopter Robots swarm
video”
The
Stanford Autonomous Helicopter performing an aerobatic airshow under computer
control:
“Stanford Autonomous
Helicopter - Airshow #1”
“Stanford Autonomous
Helicopter - Airshow #2 Redux”
No
homework --- study for the Mid-term Exam.
Tue.,
11 Nov., Veteran’s
Day Holiday. Thank you, Vets!!
Thu., 13 Nov., 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 Cultural Interest:
“hitchBOT | Making my way across
Canada, one ride at a time.”
“Canada's
hitchBOT travels 4,000 miles to test human-robot
bonds --- LA Times.”
Tue.,
18 Nov., 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].
Optional Reading:
Cyc is a large-scale knowledge-engineering project:
“CYC: A Large-Scale Investment in Knowledge Infrastructure,” Lenat, 1995
“Searching for Commonsense: Populating Cyc from the Web,” Matuszek et al, AAAI 2005
Cyc - Wikipedia, the free encyclopedia.
Optional
Ungraded Homework:
Thu., 20 Nov., 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
Lecture slides: First Order Logic Inference [PDF; PPT].
Read in advance: Textbook
Chapter 9.1-9.2, 9.5.1-9.5.5.
Tue., 25 Nov., Quiz
#3 (answer key here);
Probability, Uncertainty, Bayesian Networks.
(Lecture by Robert Hasselbeck.)
Read in advance: Textbook Chapters 13, 14.1-14.2.
Lecture
slides (two parts):
(1)
Reasoning Under Uncertainty [PDF;
PPT].
(2)
Bayesian Networks [PDF;
PPT].
Optional
Cultural Interest:
Video of Judea Pearl’s 2011 Turing Award lecture.
The Mechanization of Causal
Inference: A “mini” Turing Test and Beyond.
Optional URL: “Peter Norvig 12. Tools of AI: from logic to probability.”
Optional
Cultural Interest:
“Flexible Muscle Based
Locomotion for Bipedal Creatures” --- video
“Flexible Muscle-Based Locomotion for Bipedal Creatures” --- paper.
Tue.,
2 Dec., start Learning from Examples.
Read in advance: Textbook Chapter 18.1-18.4.
Lecture
slides: Intro to Machine Learning [PDF; PPT].
Optional
Reading:
Ferrucci, et al., 2010, “Building
Watson: An Overview of the DeepQA Project”
“Machine learning”
- Wikipedia, the free encyclopedia
“Data mining” -
Wikipedia, the free encyclopedia
Optional URL: “Google reveals it is developing a computer so smart it can program ITSELF.”
Optional Ungraded Homework:
Thu., 4 Dec., finish Learning from Examples.
Read in advance: Textbook Chapter 18.5-18.12, 20.1-20.3.2.
Lecture slides:
Learning Classifiers [PDF; PPT].
Optional
Lecture slides: Viola & Jones, Learning, Boosting, Vision [PDF; PPT]
(read the two papers 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”
Optional Ungraded Homework:
Tue., 9 Dec., Quiz #4 (answer key here);
Clustering (unsupervised learning) and Regression
(statistical numeric learning).
Read in advance: Textbook Chapter 18.6.1-2, 20.3.1.
Lecture slides:
Clustering (Unsupervised Learning) [PDF; PPT].
Optional
Cultural Interest:
“IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer”
“Speech Recognition Breakthrough for the Spoken, Translated Word”
Thu., 11 Dec.., Catch-up, Review for Final Exam.
Read in advance: Textbook, review all assigned reading.
Lecture
slides: Review, Catch-up, Question&Answer [PDF; PPT].
Tue.,
16 Dec., 7:00-9:00pm. (answer key here)
Connect-K Game.
This project corresponds to Game Search (Chapter 5 in your book). Your
job is to write an AI agent that can beat you at Connect-K, i.e., to write the
adversarial search (game search) controller for a video game world. Shells will
be available in C++ and Java. I
expect to be able to run a tournament within which your AI controllers will
compete against each other for Bonus Points. Everyone’s AI will be
entered in the tournament automatically; the bonus points are simply free,
based on how many games your AI wins against other AIs.
The Project Report template
is available here [Word; PDF].
An example
dumb game is available; an example
smart game is available; a Project Specification
is available; a Report Template is available [Word; PDF]; a
collection of student coding
resources is available.
The coding resources
include:
(1) A Java shell.
(2) A C++ shell.
(3) A tournament shell,
which will let you play different versions of your AI against themselves to
refine your evaluation function.
(4) Three example AIs,
which you or your AI can play against: a good AI, an average AI, and a poor AI.
(5) The “DummyAI” source code, which your cleverness and
ingenuity will make smart.
(6) Several readme*.txt
files: readme.txt, readme-cPlusPlus.txt, readme-tournament.txt.
(7) ConnectK
hints, caveats, and heuristics.
(8) A changelog.txt.
Note: We'll run the
tournament on SGE or a lab machine. The C++ target platform should be x86. You
should write your code to run on any x86 machine. The
OS is CentOS 6. We most likely will need to compile your code with CentOS 6
(RHEL 6) x86_64. Machines in the openlab.ics.uci.edu (family-guy.ics.uci.edu)
are CentOS 6.
Several of my various
CS-171 projects were written by former CS-171 students who became interested in
AI and signed up for CS-199 in order to pursue their interest and write
interesting AI project shells.
Please let me know if this is of interest to you (CS-171 grade of A- or
better required).
Sudoku: This project corresponds to Constraint Satisfaction Problems
(Chapter 6 in your book). Your job is to write an AI agent that can solve
“Monster Sudoku” better than you can.
Standard Sudoku is
played on a 9x9 grid subdivided into nine 3x3 boxes. Every row, column, and box
must contain the digits 1 through 9 exactly once. Monster (or Mega) Sudoku is
similar, but the grid and boxes are bigger. 12x12 puzzles are played with the
numbers 1 to 12 (or 1 to 9 and A, B, and C) in each row, column, and 3x4 box.
16x16 puzzles are played with 1 to 16 (or 1 to 9 and the letters A to G) in
each row, column, and 4x4 box. In general, NxN
puzzles are played with the numbers 1 to N (or 1 to 9 and the letters A to Z)
and pxq boxes, where N = pq. Sometimes zero is added to the digits. The Sudoku
community has developed many clever variants and encodings based on this
general idea.
See for example:
http://www.dailysudoku.com/sudoku/archive.shtml?type=monster
http://www.universaluclick.com/games/sudokumonster
http://www.conceptispuzzles.com/index.aspx?uri=puzzle/sudoku/mega
or just do a Web search for Monster (or Mega) Sudoku.
A previous offering of CS-171 coded a
Sudoku solver using the Constraint Satisfaction Problem (CSP) methods we will
study, but the methods proved to be too powerful for Sudoku and even the basic
methods could solve hard Sudoku puzzles easily. I hope Monster Sudoku will provide more
of a challenge.
The coding resources
include:
(1) A Java shell
(I do not yet have a shell in any other language; but would like to).
(3) Shell
documentation (incomplete).
A former CS-171 student
wrote the shell in Spring Quarter, 2014, so it has not yet been tested
“in action.” I will give Bonus Points to students who find and fix
bugs. He ran out of time in the quarter before he finished the documentation. I
will give Bonus Points to students who improve the documentation.
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.
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.]
This is the same basic problem as
robot navigation through a crowded workspace 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.
The coding resources include:
(1) A Java shell.
(2) A C++ shell.
(3) An example game.
(4) Examples
of maze agents in mazes.
(5) Test mazes (to appear soon). I will give Bonus Points to students who
contribute difficult mazes (should be harder than the mazes here).
It is easy to prove to yourself that
the shortest path through a maze must be a sequence of straight line segments
from polygon corner to polygon corner (apply the Triangle Inequality). The
shell is set up to facilitate finding such a solution.
Clever students have discovered that
the isVisible function is buggy, in the sense that it
is only correct for polygon corners, and sometimes allows a search path to
“tunnel through” a polygon if a candidate point is placed in the
interior of a polygon (example 1; example 2). Such paths are not considered valid
solutions. I will give Bonus Points
to students who find and fix such bugs (must be an efficient bug fix, i.e., not
slow things down too much).
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.
Also, a
student has recommended ‘quizlet.com’ as a good online study
resource. While I cannot vouch for it, apparently it contains several good
study aids for your textbook.
Fall Quarter 2014:
Mid-term Exam and key.
Final Exam and key.
Winter Quarter 2014:
Mid-term
Exam and key
Final
Exam and key
Fall Quarter 2013:
Mid-term
Exam and key
Final Exam and key
Fall Quarter 2012:
Mid-term Exam and key
Final
Exam and key
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:
Additional Online Resources may be posted as the class progresses.
Textbook website for Artificial Intelligence: A Modern Approach (AIMA).
AAAI
Digital Library of more
than 10,000 AI technical papers.
AAAI AI Magazine.
AAAI Author Kit.
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 Academic Honesty.
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.
