CompSci (CS) 271 — Introduction to Artificial Intelligence — Winter 2013

I believe that I have now fixed all of the broken links below. Please notify me by email if you find a link that does not seem to work properly. One Bonus Point will be awarded for each detected broken link.

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The Computer Science MS Comprehensive Exam

Study Guides --- Previous CS-271 Quizzes, Mid-term, and Final exams

Current Announcements:

· The CS-271 Final Exam key is posted below and is also available here.

· Please send your project team member’s names and ID#s, and your team name, to the tournament directors:

Alex Van Buskirk <avanbusk@uci.edu> (especially for the Java version)

Thomas Bennett <tebennet@uci.edu> (especially for the C++ version)

· As announced on the class mailing list, the tournament settings will change very slightly:

7 rows, 9 columns, 5 in a row to win, gravity off

In case of tie:

7 rows, 11 columns, 6 in a row to win, gravity off

The reason for this is to try to avoid a forced win for the first player.

· As announced in lecture, the Project due date is extended to the midnight at the close of Monday, 18 March. Bonus points will be awarded to students who turn it in by the original due date, the midnight at the close of Friday, 15 March.

· The Project Report template is posted below and is also available here [Word; PDF]. As announced in lecture, it is intended to be fairly simple and not burdensome.

· The answer key to Quiz #4 is posted below and is also available here. In order to help you study for the Final Exam, I also have provided you an extensive analysis of common errors. Please study it carefully to ensure that you understand it thoroughly and will get it right if similar questions appear again.

· I believe that I have now fixed all of the broken links below. Please notify me by email if you find a link that does not seem to work properly. One Bonus Point will be awarded for each detected broken link.

· As announced in class, the pedagogical device of Problem #2 of Quiz #3 is now extended to all problems of Quiz #3. To recover 50% of your missed points on Problem #1 or #3, include a brief description of how you have repaired your knowledge base so that the mistake will not happen again --- this means, what do you now understand that you did not understand before, so that if the problem (say) were to appear again on the Final Exam, you would answer the problem correctly.

· The tournament directors and coders have released new versions of the shells that improve a few minor things --- you don’t have to change your code. You may wish to use these improved shells; see the Project section.

· Problem #2 of Quiz #3 is now a pedagogical device (available here). You may, if you wish, recover 50% of your missed points by showing that you have debugged and repaired your knowledge base. For each item on which you lost points:

(a) write in the (incorrect) answer that you provided;

(b) provide an equivalent English sentence, i.e., one that is true in exactly the same possible worlds; and

To recover 50% of your points, turn in your completed exercise WITH YOUR QUIZ in class Thursday, 14 March.

Please staple the pages to your quiz.

· The Quiz #3 answer key is posted below and is also available here.

· As announced in lecture last week, the required reading for discussion this Thursday, 28 Feb, is:

Searching for Commonsense: Populating Cyc from the Web, Matuszek et al, AAAI 2005

(URL (background) https://www.aaai.org/Library/AAAI/2005/aaai05-227.php)

(URL (paper) https://www.aaai.org/Papers/AAAI/2005/AAAI05-227.pdf)

It had been displaced in the syllabus below, but now is returned to its correct place.

· The Mid-term Exam key has been posted below and is also available here.

· A new version of the C++ project shell is available. It now passes in the win parameter K corresponding to the number of aligned squares needed to win the game. The new version is available here, and below in the Project section.

· In order to reduce the amount of reading and focus on the most interesting passages, the required reading of "Deep Blue" for Thursday, 31 Jan, is hereby restricted to the following sections: 2, 3.2, 3.3, 7.1, 7.3, 8.1, 8.4, & 9.

· Project shells and a project specification have been released. See the Project section below.

· All confirmations for the CS MS Exam that I am aware of have been sent. If you intend to have this class qualify for the CS MS Exam, and you have not received a confirmation, please notify me immediately.

· I have created two CS-271 MessageBoard forums at EEE:

(1) Class Discussion; and

(2) Seeking project programming team partner.

Place, Time, Instructor:

Lecture Place: DBH-1300 (DBH = Donald Bren Hall, building 314 on the UCI campus map)
Lecture Time: Tuesday/Thursday 12:30-1:50pm

Discussion Place: DBH-1420

Discussion Time: Tuesday 2:00-2:50pm

The Discussion section is optional. It provides an informal setting to explore topics in more depth, to work concrete examples of abstract material presented in lecture, or to get help in understanding difficult parts of the material. (Note: If no students arrive, I will leave after about 10 or 15 minutes.)

Instructor: Richard Lathrop
Office hours: Thursdays 2:00-3:00pm, or anytime by appointment, in DBH-4224

Email: rickl@uci.edu

(If you send email, please put “CS-271” 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.

Please Note:

This class is intended as a broad introduction to AI for graduate students who have had minimal or no exposure to AI previously. Students who have taken strong AI courses at the undergraduate level may wish to skip or waive this course, and instead take a more specialized course in AI or machine learning immediately.

· In particular, if you previously have had a strong introductory course in AI from the Russell&Norvig textbook, then most of the material covered here will seem familiar and redundant to you. You strongly should consider proceeding directly to a more specialized course in AI or machine learning.

Class Setup:

The course will be primarily lecture-based. There will be several quizzes, a Mid-term Exam, a Final Exam, and an AI coding project.

On every second Tuesday as adjusted for the Mid-term Exam, 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.

On every Thursday except before Exams, roughly the first hour will be lecture, and the final 15-20 minutes will be open discussion of the weekly background reading. Your active participation in the discussion is a required part of the course, and you are not allowed to leave class when the reading discussion begins. I believe it is important at the graduate level for students to grapple with and engage in the intellectual content of the course 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 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. 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; it shocked me to encounter this level of personal degradation at the graduate level, but I cannot control it and 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, 3^rd edition.

The course is based on, and the UCI bookstore has, the 3^rd 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.

R&N estimates that about 20% of the material in the 3^rd edition is new from the 2^nd edition. Several of the chapters and exercises have been rearranged. (A kind and helpful student has elucidated some of the differences between the 2^nd & 3^rd 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, Chegg.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&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%), an AI coding project (20%), a mid-term exam (25%), and a final exam (35%). Your participation in the open discussion on Thursdays is required but ungraded, though you will be penalized if you do not participate actively.

· 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 Tue., 12 Feb., and is closed-book, closed-notes. It is not possible to make-up a missed mid-term exam.

· The final exam will be given on Fri., 22 Mar., 10:30am-12:30pm (NOT the regular class time), and is closed-book, closed-notes. The final exam will cover all course material from the entire quarter, but mostly the second half. It is not possible to make-up a missed final exam.

Dates and times for all final exams are set by the UCI Registrar (Final Exam Schedule 2012-13).

I make exceptions for genuine medical conditions (I require a note from your doctor on official letterhead) or deaths in the family (I require a copy of the death certificate). Also, I honor all requests made by the UCI Disability Services Center.

· Everyone is expected to read the background reading, and to come to class prepared to engage in graduate-level intellectual discussion of the subject matter. Arrive in class with: (1) a general understanding of the reading; (2) one interesting point about the reading; and (3) one interesting question about the reading. [Note: Due to the large class size, not everyone may get to talk each session, but you are required to participate actively at least once as the term goes along.]

· Every student who fills out a course evaluation for CS-271 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. A bonus point is equivalent to a quiz point.

The Computer Science MS Comprehensive Exam:

You must notify me if you intend to use this course to satisfy the CS M.S. Comprehensive Exam requirement (info from the ICS SAO appears here). If you so intend, please email me IMMEDIATELY stating your intention, and no later than one week before the Mid-term Exam.

Please include your full name and your UCI Student ID#. Send the message from your UCI email account (to ensure integrity and prevent identity theft).

The UCI Registrar will not allow me to post a list of students who have so notified me (because of student privacy regulations). You are NOT registered for the CS MS Exam UNLESS you have received an email message from me that states explicitly, “I hereby confirm you for the CS-271 CS MS Exam.”

Your score for the CS MS Exam will be based only on your mid-term exam (50%) and final exam (50%). You will pass the CS MS Exam if, and only if, your MS Exam score would result in a passing grade (i.e., a grade of “B” or better) in CS-271 for Winter Quarter 2013.

· Please note that, in some borderline cases, it may be possible to pass the course but fail the MS Exam, and vice versa; but this should be rare.

If you are NOT enrolled in this class AND you plan to take the CS MS Exam for CS-271 this quarter in this class:

· Please confirm with an ICS Graduate Counselor in which room you will take the exam. You may be asked to take the exam in a different room if the seating capacity of the CS-271 classroom would be exceeded.

· Please note that the class period before the mid-term and final exams will be a Review, Catch-up, Question&Answer overview session. You are welcome to attend these two sessions on a standing-room-only basis. Enrolled students have absolute priority for seats, due to the limited room seating capacity. Please remain standing quietly at the back of the room for the first five (5) minutes of the class. After 5 minutes have elapsed, then you may occupy the remaining vacant seats.

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.

Detailed Schedule, 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 just prior to the lecture; please ensure that you have the current version.

Week 1:

Tue., 8 Jan., Introduction, Agents.

Read in Advance: Textbook Chapters 1-2.

Lecture slides: Introduction, Agents [PDF; PPT].

Optional Cultural Interest:

IBM's Watson supercomputer destroys all humans in Jeopardy

AI vs. AI. Two chatbots talking to each other.

How IBM's Watson supercomputer wins at Jeopardy, with IBM's Dave Gondek

IBM Watson: Final Jeopardy! and the Future of Watson

Thu., 10 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].

Required Reading for next discussion: John McCarthy, “What Is Artificial Intelligence?”

(URL http://www-formal.stanford.edu/jmc/whatisai.pdf)

Optional Reading: HTML and other versions of “What is AI?”

Optional URL: John McCarthy Homepage
Optional Cultural Interest:

Honda's robot ASIMO

Boston Dynamics Big Dog (new video March 2008)

Week 2:

Tue., 15 Jan., start Heuristic Search.

Read in advance: Textbook Chapter 3.5-3.7.

Lecture slides: Heuristic Search [PDF; PPT]

Optional Cultural Interest:

“Singularity Institute for Artificial Intelligence- P1/2 - Video Dailymotion”

“Singularity Institute for Artificial Intelligence”

“Singularity Institute - Wikipedia, the free encyclopedia”

Singularity Institute home 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.”

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., 17 Jan., finish Heuristic Search, start Local Search; discuss previous Required Reading.

Read in advance: Textbook Chapter 4.1-4.2.

Lecture slides: Heuristic Search (above); Local Search [PDF; PPT].

Required Reading for next discussion:
Minton, et. al., 1990, AAAI "Classic Paper" Award recipient in 2008.

How to solve the 1 Million Queens problem and schedule the Hubble telescope.

(URL http://www.aaai.org/Papers/AAAI/1990/AAAI90-003.pdf)

Optional Reading: “Autonomous Driving in Traffic: Boss and the Urban Challenge” (2009).

(URL http://www.ri.cmu.edu/pub_files/2009/6/aimag2009_urmson.pdf)

Optional Reading: “Stanley: The Robot that Won the DARPA Grand Challenge” (2005).

Optional Reading: DARPA Grand Challenge Wikipedia info.

Optional URL: DARPA Grand Challenge website.
Optional Cultural Interest:

DARPA Urban Challenge Highlights

Princeton DARPA Grand Challenge - Crash Video

DARPA Urban Challenge: Ga Tech hits curb

DARPA Urban Challenge - Sting Racing crash

Team Oshkosh attempts forced Entry to Main Exchange

Alice's Crash (spectator view)

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:

Homework #1; answer key.

Week 3:

Tue., 22 Jan., Quiz #1 (answer key here); finish Local Search.

Read in advance: Textbook Chapters 3, 4.1-4.2.

Lecture slides: Local Search (above); Review Search [PDF; PPT].

Optional Cultural Interest:

Google Goggles

Thu., 24 Jan., start Games/Adversarial Search; discuss previous Required Reading.

Read in advance: Textbook Chapter 5.1-5.5.

Lecture slides: Games/Adversarial Search [PDF; PPT].

Required Reading for next discussion:

Campbell, et al., 2002, Artificial Intelligence, “Deep Blue.” [PDF]

(URL http://www.sciencedirect.com/science/article/pii/S0004370201001291)

The required reading is only sections 2, 3.2, 3.3, 7.1, 7.3, 8.1, 8.4, & 9.

Optional Reading: Newell & Simon’s “Symbols and Search” Turing Award Lecture (1976).

Herbert Simon was awarded a Nobel Prize (in economics, 1978).

Optional URL: “Boxcar 2D”

The program learns to build a car using a genetic algorithm.

It starts with a population of 20 randomly generated shapes with wheels and runs each one to see how far it goes.

The cars that go the furthest reproduce to produce offspring for the next generation.

The offspring combine the traits of the parents to hopefully produce better cars.

Optional Ungraded Homework:

Homework #2; answer key.

Week 4:

Tue., 29 Jan., finish Games/Adversarial Search; overview Representation.

Read in advance: Textbook Chapter 5.1-5.5.

Lecture slides (two parts):

(1) Games/Adversarial Search (above).

(2) Representation [PDF; PPT].

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.

A game we won’t study in this class --- Robot Soccer.

RoboCup 2012 Standard Platform: USA / Germany (Final).

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.

Thu., 31 Jan., start Constraint Satisfaction; discuss previous Required Reading.

Read in advance: Textbook Chapter 6.1-6.4, except 6.3.3.

Lecture slides: Constraint Satisfaction Problems [PDF; PPT].

Required Reading for next discussion:

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.

Optional Reading: AAAI/AI Topics: The Turing Test: “Can Machines Think?”

Optional Reading: Wikipedia “Computing Machinery and Intelligence”

Optional Cultural Interest:

Audi's automatic driving for parking.

Optional Ungraded Homework:

Homework #3; answer key.

Week 5:

Tue., 5 Feb., Quiz #2 (answer key here); finish Constraint Satisfaction.

Read in advance: Textbook Chapter 6.1-6.4, except 6.3.3.

Lecture slides: Constraint Satisfaction Problems (above).

Thu., 7 Feb., Catch-up, Review for Mid-term Exam; NO Required Reading discussion, due to exam.

Read in advance: Textbook Chapters 1-2, (as above).

Lecture slides: Catch-up, Review, Question&Answer [PDF; PPT].

Required Reading for next discussion: Same as last week, Turing’s paper.

Week 6:

Tue., 12 Feb., Mid-term Exam (answer key here).

Read in advance: Textbook Chapter 1-6 (as restricted above).

No lecture slides, due to exam.

Thu., 14 Feb., Review Mid-term Exam; start Propositional Logic; discuss previous Required Reading.

Read in advance: Textbook Chapter 7.1-7.4.

Lecture slides: Propositional Logic A [PDF; PPT].

Required Reading for next discussion:

Ferrucci, et al., 2010, “Building Watson: An Overview of the DeepQA Project”

(URL https://www.aaai.org/ojs/index.php/aimagazine/article/view/2303/2165)

Optional Ungraded Homework:

Homework #4; answer key.

Week 7:

Tue., 19 Feb., finish Propositional Logic.

Read in advance: Textbook Chapter 7.5-7.8.

Lecture slides: Propositional Logic B [PDF; PPT].

Thu., 21 Feb., start Chapter 8 (First Order Logic); discuss previous Required Reading.

Read in advance: Textbook Chapter 8.1-8.3.

Lecture slides: First Order Predicate Calculus (FOPC) [PDF; PPT].

Required Reading for next discussion:

Searching for Commonsense: Populating Cyc from the Web, Matuszek et al, AAAI 2005

(URL (background) https://www.aaai.org/Library/AAAI/2005/aaai05-227.php)

(URL (paper) https://www.aaai.org/Papers/AAAI/2005/AAAI05-227.pdf)

Optional Ungraded Homework:

Homework #5; answer key.

Week 8:

Tue., 26 Feb., Quiz #3 (answer key here; pedagogy here); Finish Chapter 8 (First Order Logic).

Read in advance: Textbook Chapter 8.4-8.5.

Lecture slides (two parts):

(1) Finish FOPC [PDF; PPT]; and

(2) Knowledge Representation in FOPC [PDF; PPT].

Optional Cultural Interest:

“Quadrocopter Pole Acrobatic” (URL: https://www.youtube.com/watch?v=pp89tTDxXuI )

“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”

Thu., 28 Feb., Inference in First Order Logic; discuss previous Required Reading.

Read in advance: Textbook Chapter 9.

Lecture slides: Inference in First Order Predicate Calculus (FOPC) [PDF; PPT].

Required Reading for next discussion:

Is actually a video, Judea Pearls 2011 Turing Award lecture.

(URL: http://amturing.acm.org/vp/pearl_2658896.cfm )

The Mechanization of Causal Inference: A “mini” Turing Test and Beyond.

Optional Ungraded Homework:

Homework #6; answer key.

Week 9:

Tue., 5 Mar., Probability, Uncertainty, Bayesian Networks.

Read in advance: Textbook Chapters 13, 14.1-14.2.

Lecture slides (two parts):

(1) Probability, Uncertainty [PDF; PPT].

(2) Bayesian Networks [PDF; PPT].

Optional Cultural Interest:

p53 and Cancer Research - UC Irvine

Optional URL: “Peter Norvig 12. Tools of AI: from logic to probability.”

Thu., 7 Mar., start Learning from Examples; discuss previous Required Reading (video of Pearl’s Turing lecture).

Read in advance: Textbook Chapter 18.1-18.4.

Lecture slides: Intro to Machine Learning [PDF; PPT].

NO required reading for next week: study for the Final Exam.

Optional Cultural Interest:

“IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer”

“Speech Recognition Breakthrough for the Spoken, Translated Word”

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., 12 Mar., Quiz #4 (answer key here); finish Learning from Examples, start Probabilistic Learning.

Read in advance: Textbook Chapter 18.5-18.12, 20.1-20.3.2.

Lecture slides: Learning Classifiers, Boosting [PDF; PPT].

Optional Cultural Interest:

“High-Speed Robot Hand”

Thu., 14 Mar., Catch-up, Review for Final Exam; NO Required Reading discussion, due to exam.

Read in advance: Textbook, review all assigned reading

No lecture; open Question & Answer session.

Lecture slides: Review, Catch-up, Question&Answer [PDF; PPT].

No required reading: study for the Final Exam.

Project Due Date:

Fri., 15 Mar., midnight (i.e., the midnight between Friday and Saturday).

Please deposit in EEE Dropbox as instructed.

Final Exam:

Fri., 22 Mar., 10:30am-12:30pm Final Exam (answer key here)

NOTE: Final Exam time is 10:30am, NOT the regular class time of 12:30pm.

Place: DBH-1300 (DBH = Donald Bren Hall, building 314 on the UCI campus map)

Project:

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 hope to be able to run a tournament within which your AI controllers will compete against each other for Bonus Points.

The Project Report template is available here [Word; PDF].

A Java shell is available; a C++ shell is available; an example dumb game is available; an example smart game is available; a Project Specification is available.

A tournament shell is available (download cppTournament.zip) if you wish to play different versions of your AI against themselves to refine your evaluation function.

Please contact the tournament directors and coders directly with any questions or concerns:

Alex Van Buskirk <avanbusk@uci.edu> (especially for the Java version)

Thomas Bennett <tebennet@uci.edu> (especially for the C++ version)

[Normally I am very protective of student privacy; in this case, they have volunteered to make their names and email addresses available to you.]

You are required to form project teams of two students. It is not possible to have one-person project teams.

Please follow the Pair Programming paradigm. One person types the code while the other looks over their shoulder, and they switch roles frequently.

Study Guides --- Previous CS-271 Quizzes, Mid-term, and Final exams:

Previous CS-271 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 2013:

Quiz #1 and key

Quiz #2 and key

Quiz #3 and key and pedagogy

Quiz #4 and key

Mid-term Exam and key

Final Exam and key

Fall Quarter 2011:

Quiz #1 and key

Quiz #2 and key

Quiz #3 and key

Quiz #4 and key

Mid-term Exam and key

Final Exam and key

Fall Quarter 2010:

Quiz #1 and key

Quiz #2 and key

Quiz #3 and key

Quiz #4 and key

Mid-term Exam and key

Final Exam and key

Spring Quarter 2010:

Mid-term Exam key

Final Exam key

[No Quizzes; Homeworks were given instead.]

Online Resources:

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. (apparently has gone away in recent website reorganization?)

AAAI Classic Papers.

AAAI Annual Conference.

AAAIs digital library of more than 10,000 AI technical papers

AAAIs AI Magazine

AAAIs Author Instructions

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 Bren School Policies 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.

CompSci (CS) 271 — Introduction to Artificial Intelligence — Winter 2013

I believe that I have now fixed all of the broken links below. Please notify me by email if you find a link that does not seem to work properly. One Bonus Point will be awarded for each detected broken link.

Current Announcements:

Place, Time, Instructor:

Lecture Place: DBH-1300 (DBH = Donald Bren Hall, building 314 on the UCI campus map) Lecture Time: Tuesday/Thursday 12:30-1:50pm

Discussion Place: DBH-1420

Discussion Time: Tuesday 2:00-2:50pm

Instructor: Richard Lathrop Office hours: Thursdays 2:00-3:00pm, or anytime by appointment, in DBH-4224

Email: rickl@uci.edu

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.

Please Note:

· In particular, if you previously have had a strong introductory course in AI from the Russell&Norvig textbook, then most of the material covered here will seem familiar and redundant to you. You strongly should consider proceeding directly to a more specialized course in AI or machine learning.

Class Setup:

The course will be primarily lecture-based. There will be several quizzes, a Mid-term Exam, a Final Exam, and an AI coding project.

On every second Tuesday as adjusted for the Mid-term Exam, 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.

Textbook:

Required: Russell & Norvig : Artificial Intelligence; A Modern Approach, 3rd edition.

Grading:

Your grade will be based on the bi-weekly quizzes (20%), an AI coding project (20%), a mid-term exam (25%), and a final exam (35%). Your participation in the open discussion on Thursdays is required but ungraded, though you will be penalized if you do not participate actively.

· The mid-term exam will be given in class on Tue., 12 Feb., and is closed-book, closed-notes. It is not possible to make-up a missed mid-term exam.

· The final exam will be given on Fri., 22 Mar., 10:30am-12:30pm (NOT the regular class time), and is closed-book, closed-notes. The final exam will cover all course material from the entire quarter, but mostly the second half. It is not possible to make-up a missed final exam.

Dates and times for all final exams are set by the UCI Registrar (Final Exam Schedule 2012-13).

I make exceptions for genuine medical conditions (I require a note from your doctor on official letterhead) or deaths in the family (I require a copy of the death certificate). Also, I honor all requests made by the UCI Disability Services Center.

· Every student who fills out a course evaluation for CS-271 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.

The Computer Science MS Comprehensive Exam:

Your score for the CS MS Exam will be based only on your mid-term exam (50%) and final exam (50%). You will pass the CS MS Exam if, and only if, your MS Exam score would result in a passing grade (i.e., a grade of “B” or better) in CS-271 for Winter Quarter 2013.

· Please note that, in some borderline cases, it may be possible to pass the course but fail the MS Exam, and vice versa; but this should be rare.

If you are NOT enrolled in this class AND you plan to take the CS MS Exam for CS-271 this quarter in this class:

· Please confirm with an ICS Graduate Counselor in which room you will take the exam. You may be asked to take the exam in a different room if the seating capacity of the CS-271 classroom would be exceeded.

Week 1:

Week 2:

Week 3:

Week 4:

Week 5:

Week 6:

Week 7:

Week 8:

Week 9:

Week 10:

Project Due Date:

Final Exam:

Project:

Study Guides --- Previous CS-271 Quizzes, Mid-term, and Final exams:

Online Resources:

Academic Honesty:

Lecture Place: DBH-1300 (DBH = Donald Bren Hall, building 314 on the UCI campus map)
Lecture Time: Tuesday/Thursday 12:30-1:50pm

Instructor: Richard Lathrop
Office hours: Thursdays 2:00-3:00pm, or anytime by appointment, in DBH-4224

Required: Russell & Norvig : Artificial Intelligence; A Modern Approach, 3^rd edition.

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.