CompSci (CS) 171 — Introduction to Artificial Intelligence — Fall Quarter 2016

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Center for Machine Learning and Intelligent Systems

UCI/ICS AI Faculty and Their Suggested Papers for CS-171

Syllabus Overview and Important Dates

Final Exam (Tue., 6 Dec., 4:00 - 6:00 p.m.)

Study Guides --- Previous CS-171 Tests

Current Announcements:

v Current announcements will appear here, at top-level, for quick and easy inspection.

Time, Place:

Lecture:

Place: ELH 100 (Engineering Lecture Hall; Building 305 on the UCI campus map)
Time: Tuesday/Thursday, 3:30- 4:50pm

Coding Project Help:

· “Project Questions and Answers” in ELH-100, at the beginning of every lecture throughout the quarter, for brief immediate answers to project questions.

· “Project Clinic” in DBH-3013, every Monday 4-5p and Wednesday 5-6pm throughout the quarter, for in-depth personal help with project coding issues.

Discussion section is REQUIRED and roll will be taken.

Discussion section 1:

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 1:00- 1:50pm

Discussion section 2:

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 2:00- 2:50pm

Discussion section 3:

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 3:00- 3:50pm

Discussion section 4:

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 4:00- 4:50pm

Teaching Staff:

Instructor:

Alex Ihler
Office hours: TBA, or anytime by appointment, in DBH room 4066.

Email: aihler@uci.edu or ihler@ics.uci.edu

(If you send email, please put “CS-171” somewhere in the Subject line.)

Coding Project Tournament Director:

Toluwanimi Salako

Office hours: “Project Clinic” Mon. 4-5pm/Wed. 5-6pm in DBH-3013.

This time will be a “Project Clinic” where you can get answers to project questions and help with project problems. Tolu also will attend the first part of each class to answer project questions.

(If you send email, please put “CS-171” somewhere in the Subject line.)

TAs:

TBA #1

Office hours: TBA, or anytime by appointment, in TBA.

Email: TBA

(If you send email, please put “CS-171” somewhere in the Subject line.)

TBA #2

Office hours: TBA, or anytime by appointment, in TBA.

Email: TBA

(If you send email, please put “CS-171” somewhere in the Subject line.)

[... repeat TBA TAs as needed ...]

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 other Thursdays before the Mid-term Exam, and every other Tuesday after it, the first 20 minutes will be an in-class pop quiz, followed by lecture (see specific dates in Syllabus Overview ). 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 many questions intended to encourage you to remember the earlier material (i.e., the Final Exam will be comprehensive). Please study the previous CS-171 quizzes and exams (below), which are made available as study guides to help you learn and master the class material; they are important guides about the performance that will be expected from you now.

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.

There will be an AI coding project (see Project section below). This is an individual or pair project, i.e., you must do it entirely by yourself or form a team of two people.

Sometimes teams do not work out. Any team member may dissolve their team at any time by so notifying the Instructor, TAs, and Tournament Director. Any code written before the dissolution is “community property” which may be used freely by both former team members. Any code written after the dissolution is the sole property of the person who wrote it.

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 or your team must invent and write all of your own code by yourself. Except for properly referenced material, you or your team must write all of your own project report by yourself.

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).

Please start your AI coding project earlier than you believe necessary, i.e., immediately; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

All the various CS-171 AI project shells 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 more interesting AI project shells. Please let me know if this is of interest to you (CS-171 grade of A- or better required).

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. 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 always will be available when you need them. Please purchase or rent your own personal textbook. Otherwise, you are at a severe disadvantage.

I do deplore the high cost of modern textbooks. You may find the textbook cheaper if you look online at sites such as eBay.com, Amazon.com, etc.; or search the web for other sites related to the textbook.

Grading:

Your grade will be based on Discussion Section participation (10%), a coding project (20%), the four quizzes (20%), a mid-term exam (25%), and a final exam (25%). Homework is assigned but ungraded.

· Discussion Section is REQUIRED and roll will be taken each period (10 periods = 1 period per week over 10 weeks). Each of the 10 periods counts as 1/10 of the 10% Discussion Section Participation points. You may not leave after roll has been taken and before the end of the period, i.e., it is not OK to show up for roll and then depart prematurely.

· · The AI coding project will be a “Connect-K” AI agent. “Dumb” coding shells are available in C++, Java, and Python. You must write the “smarts.” This is a solo or pair project and you must do all of it all by yourself or with a single team-mate.

You are already behind schedule. Start coding now. More details below in the Project section. You will lose 10% of your Project grade for every day or fraction thereof your project submission is late.

Your “Final AI” will be entered into a tournament against all of your classmate’s Final AIs. The top 10% will have their Project score increased by 10% (= 2% of total grade), the second 10% by 9%, the third 10% by 8%, and so on.

· · Quizzes will be given the first 20 minutes of class every second Thursday before, and each second Tuesday after, the Mid-term Exam (dates are listed in the Syllabus Overview below), and are closed-book, closed-notes. 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 Tuesday, 1 Nov., and is closed-book, closed-notes. It is not possible to make-up a missed Mid-term exam.

· · The Final exam will be given in class on Tuesday, 6 Dec., 4-6pm, and is closed-book, closed-notes. The Final exam will cover all course material from the entire quarter, with emphasis on the second half. It is not possible to make-up a missed Final exam.

I honor all requests made by the UCI Disability Services Center.

Also, I make exceptions for:

* genuine medical conditions (I require a signed note from your doctor on official letterhead),

* births/deaths in the family (I require a copy of the birth/death certificate),

* jury duty or other court proceedings (I require a copy of your jury service papers or other official court documents), or

* field maneuvers of the US military or National Guard (I require a copy of your official orders).

· · 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 (minor 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.

Your Bonus Points, if any, should be visible to you in EEE GradeBook. If for some reason you have been awarded a Bonus Point, but you did not get a notification from me or it did not appear in EEE GradeBook, please do not hesitate to send an email message to me as a reminder.

Study Habits:

This course is technical, rigorous, and demanding. You will be expected to learn and master a large body of technical material in a very short period of time. You must demonstrate your mastery by (1) accurate performance on frequent quizzes and exams, and (2) successful implementation of an AI coding project.

I deliberately treat you as adults who are responsible for your own educational decisions, and so Lecture is optional. Discussion Section is required and roll will be taken, because it is part of our educational mission to train our TAs to become future professors. Nevertheless, students who do not attend Lecture are at a serious disadvantage and do not succeed as well in this class. Students who spend Lectures and Discussion Section sleeping, on cell phones, surfing the Web, or on social media are wasting their time and might as well be absent. Such students send me email messages to ask questions that already were covered thoroughly and in detail during Lecture and once again in Discussion Section. On quizzes and exams, they miss points that already have been covered thoroughly.

Your educational moments are precious, and your education now will be the single most important factor in your future career success or failure. Please, make the most of your precious educational moments now. Please, attend both Lecture and Discussion Section, pay attention, ask questions, and master the material.

Please do not ever fall behind in the class material; instead, study frequently and diligently. Please begin your AI coding project earlier than you believe necessary; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

Please work harder and study longer. Please understand thoroughly all class material, and ask questions when you do not understand. Please attend all lectures and discussion sections. Please come to lectures and discussion sections prepared with questions about any material that is not clear. Please do all assigned reading, both before and again after lecture. Please review the lecture notes, several times over, both before and again after lecture, until you understand every detail. Please regularly attend office hours with me and the TA. Please ask questions about any class material that is not absolutely crystal clear.

Please work and understand all past quizzes and exams; they are important guides about the performance that will be expected from you now. Please work and understand all the optional homework.

Please OVERSTUDY!!

AI at UCI/ICS:

UCI/ICS AI Research Group

Center for Machine Learning and Intelligent Systems

UCI/ICS AI Faculty and Their Suggested Papers for CS-171

Pierre Baldi:

Baldi, et al., 2014, “Deep Learning in High-Energy Physics: Improving the Search for Exotic Particles”

Baldi & Sadowsk, 2016, “A Theory of Local Learning, the Learning Channel, and the Optimality of Backpropagation”

Rina Dechter:

Dechter & Pearl, 1985, “Generalized Best-First Search Strategies and the Optimality of A*”

Dechter, et al., 1991, “Temporal Constraint Networks”

Charless Fowlkes:

Yang, et al., 2011, “Layered Object Models for Image Segmentation”

Hallman & Fowlkes, 2015, “Oriented Edge Forests for Boundary Detection” (Sam Hallman was a UCI undergraduate when he presented this paper as a talk at the Computer Vision and Pattern Recognition conference. Sam completed his PhD with me in 2015. He is now working at Amazon. --- From Charless Fowlkes)

Diaz, et al., 2016, “Lifting GIS Maps into Strong Geometric Context” (This paper uses a dataset of photos taken on the UCI campus and a 3D model of our corner of campus. --- From Charless Fowlkes)

Ren, et al., 2008, “Learning Probabilistic Models for Contour Completion in Natural Images” (This is an older paper from my PhD but has a very nice connection to the discussion of Huffman & Clowes line labeling as constraint satisfaction in Russell & Norvig, AIMA, Chap 24, 2nd ed. --- From Charless Fowlkes)

Alexander Ihler:

Sudderth, et al., 2010, “Nonparametric Belief Propagation”

Liu & Ihler, 2012, “Belief Propagation for Structured Decision Making”

Richard Lathrop:

Lathrop, et al., 1999, “Knowledge-based Avoidance of Drug-Resistant HIV Mutants” (Miriam Raphael and Sophia Deeds-Rubin were ICS undergraduate women when they shared the cover illustration of AI Magazine and an award from the Innovative Applications of Artificial Intelligence conference for this paper. --- From Richard Lathrop)

Danziger, et al., 2009, “Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning”

Wassman, et al., 2013, “Computational Identification of a Transiently Open L1/S3 Pocket for Reactivation of Mutant p53”

Eric Mjolsness:

Mjolsness, 2013, “Quantitative Symbolic Process Models: How a Fair Fraction of Intelligence Could be Abstracted in AI Research”

Mjolsness & Cunha, 2012, “Topological object types for morphodynamic modeling languages”

Mjolsness, 2010, “Towards Measurable Types for Dynamical Process Modeling Languages”

Hernando Ombao:

Wang, et al., 2016, “Modeling Effective Connectivity in High-Dimensional Cortical Source Signals”

Donald Patterson:

Patterson, et al., 2003, “Inferring High-Level Behavior from Low-Level Sensors” (This paper won a Ten-Year Impact Award. --- From Donald Patterson)

Patterson, et al., 2004, “Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services”

Babak Shahbaba:

Shahbaba & Neal, 2009, “Nonlinear Models Using Dirichlet Process Mixtures”

Lan, et al., 2014, “Wormhole Hamiltonian Monte Carlo”

Sameer Singh:

Ribeiro, et al., 2016, “ ‘Why Should I Trust You?’ Explaining the Predictions of Any Classifier” (This paper won the Audience Appreciation Award at the ACM SIG Conference on Knowledge Discovery and Data Mining (KDD); see easy to read article by us on O'Reilly; view Summary Video. --- From Sameer Singh)

Gupta & Singh, 2016, “Collective Factorization for Relational Data: An Evaluation on the Yelp Datasets” (Nitish Gupta was an undergraduate who shared in the publication of this work, which was a grand prize winner in Round 4 of the Yelp dataset challenge. --- From Sameer Singh)

Padraic Smyth:

Gaffney, et al., 2007, “Probabilistic clustering of extratropical cyclones using regression mixture models” (The key student involved in this work started out as an ICS undergraduate. Scott Gaffney took ICS 171 and 175, got interested in AI, started to work in my group, decided to stay in ICS for his PhD, did a terrific job in writing a thesis on curve-clustering and working with collaborators in climate science to apply it to important scientific problems, and is now one of the leaders of Yahoo! Labs reporting directly to the CEO there. Scott grew up locally in Orange County and is someone I like to point as a great success story for ICS. --- From Padhraic Smyth)

Hal Stern:

Heins & Stern, 2014, “A Statistical Model for Event Sequence Data”

Xiaohui Xie:

Kim & Xie, 2014, “Handwritten Hangul recognition using deep convolutional neural networks”

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 often tweak or revise the lecture slides prior to or after 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.

Syllabus Overview and Important Dates

(Please read textbook and lecture notes before AND after each lecture)

(Please note that Coding Project Clinics are 4-5pm Mondays, and 5-6pm Wednesdays, in order to accommodate disparate conflicting schedules.)

Week	Date	Event	Lecture Topic	Lecture Reading
1	Thu 22 Sep	Lecture	Class setup, Intro Agents	Chapters 1-2
	Fri 23 Sep	Discussion Section	Review, questions
2	Mon 26 Sep	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 27 Sep	Lecture	Intro State Space Search Uninformed Search	Chapter 3.1-3.4
	Wed 28 Sep	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 29 Sep	Lecture	Heuristic Search	Chapter 3.5-3.7
	Fri 30 Sep	Discussion Section	Review, questions
	Sun 2 Oct	Team formation deadline	You must register your team name (yourself alone, or a team of at most two people).
3	Mon 3 Oct	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 4 Oct	Lecture	Local Search	Chapter 4.1-4.2
	Wed 5 Oct	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 6 Oct	Quiz #1 Lecture	Game (Adversarial) Search A	Chapter 5.1, 5.2, 5.4
	Fri 7 Oct	Discussion Section	Review, questions
	Sun 9 Oct	Minimal AI deadline	Minimal AI must run on openlab.ics.uci.edu in the tournament shell and at least make a random move on its turn
4	Mon 10 Oct	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 11 Oct	Lecture	Game (Adversarial) Search B	Chapter 5.3 (optional: 5.5+)
	Wed 12 Oct	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 13 Oct	Lecture	Constraint Satisfaction A	Chapter 6.1-6.4, except 6.3.3
	Fri 14 Oct	Discussion Section	Review, questions
5	Mon 17 Oct	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 18 Oct	Lecture	Constraint Satisfaction B	Chapter 6.1-6.4, except 6.3.3
	Wed 19 Oct	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 20 Oct	Quiz #2 Lecture	Propositional Logic A	Chapter 7.1-7.4
	Fri 21 Oct	Discussion Section	Review, questions
	Sun 23 Oct	First-Draft AI deadine	First-Draft AI must run on openlab.ics.uci.edu in the tournament shell and implement mini-max search with depth cut-off and heuristic static evaluation function
6	Mon 24 Oct	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 25 Oct	Lecture	Propositional Logic B	Chapter 7.5 (optional: 7.6-7.8)
	Wed 26 Oct	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 27 Oct	Lecture	Mid-term Exam Review	All of above
	Fri 28 Oct	Discussion Section	Review, questions
	Sun 30 Oct	Alpha-beta AI deadline	Alpha-beta AI must run on openlab.ics.uci.edu in the tournament shell and extend First-Draft AI to include alpha-beta pruning
7	Mon 31 Oct	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013	Happy Halloween! Wear a costume!
	Tue 1 Nov	Mid-term	Mid-term Exam	All of above
	Wed 3 Nov	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 3 Nov	Lecture	Predicate Logic A	Chapter 8.1-8.5
	Fri 4 Nov	Discussion Section	Review, questions
8	Mon 7 Nov	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 8 Nov	Lecture	Predicate Logic B	Review Chapters 8.3-8.5, Read 9.1-9.2 (optional: 9.5)
	Wed 9 Nov	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 10 Nov	Lecture	Probability, Uncertainty, Bayes Nets	Chapters 13, 14.1-14.5
	Fri 11 Nov	Discussion Section	Review, questions
	Sun 13 Nov	IDS/Sorting AI deadline	IDS/Sorting AI must run on openlab.ics.uci.edu in the tournament shell and extend Alpha-beta AI to include IDS search up to the time limit plus sort moves based on the last IDS iteration to put the most favorable moves first for A/B pruning
9	Mon 14 Nov	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 15 Nov	Quiz #3 Lecture	Machine Learning A	Chapter 18.1-18.4
	Wed 16 Nov	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 17 Nov	Lecture	Machine Learning B	Chapters 18.5-12, 20.1-2
	Fri 18Nov	Discussion Section	Review, questions
10	Mon 21 Nov	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 22 Nov	Lecture	Clustering, Regression	Chapter 18.6.1-2, 20.3.1
	Wed 23 Nov	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 24 Nov	Holiday!	Happy Thanksgiving Holiday!	Rejoice, give thanks.
	Fri 25 Nov	Holiday!	Happy Thanksgiving Holiday!	Rejoice, give thanks.
11	Mon 28 Nov	Project Clinic	Coding Project Clinic, 4-5pm, DBH-3013
	Tue 29 Nov	Quiz #4 Lecture	Special Topics Lecture (1) Special topics request from students; (2) TA or Prof present their own research; (3) Catch-up lecture if needed; (4) Any other use desired.	TBA
	Wed 30 Nov	Project Clinic	Coding Project Clinic, 5-6pm, DBH-3013
	Thu 1 Dec	Lecture	Final Exam Review	All of the above
	Fri 2 Dec	Discussion Section	Review, questions
	Sun 4 Dec	Final AI deadline	Final AI must run on openlab.ics.uci.edu in the tournament shell and extend IDS/Sorting AI to be your best and final AI that will compete for you in the tournament. Let the Games begin!
12	Tue 6 Dec	Final Exam	4:00 - 6:00 p.m.	All of the above

Week 1:

Thu., 22 Sep:

Lecture: Class setup, Introduction, Agents.

Read in advance: Textbook Chapters 1-2.

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

Fri., 23 Sep:

Discussion Section (required): Review material for this week.

Week 1 Optional Mid-class Video Break (show both for contrast):

IBM Watson: Final Jeopardy! and the Future of Watson

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

Week 1 Optional Cultural Interest:

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”

John McCarthy, “What Is Artificial Intelligence?”

AAAI, AI Overview.

Silicon Valley Kingpins Commit $1 Billion to Create Artificial Intelligence Without Profit Motive

“Technological singularity” --- Wikipedia.

“The technological singularity hypothesis is that accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization in an event called the singularity.”

“The Coming Technological Singularity: How to Survive in the Post-Human Era” (c) 1993 by Vernor Vinge.

(Verbatim copying/translation and distribution of this entire article is permitted in any medium, provided this notice is preserved.)

“Abstract: Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended. Is such progress avoidable? If not to be avoided, can events be guided so that we may survive? These questions are investigated. Some possible answers (and some further dangers) are presented.”

“.... Just so I'm not guilty of a relative-time ambiguity, let me more specific: I'll be surprised if this event occurs before 2005 or after 2030....”

Rumors, and rumors of rumors. Skeptics, including me, point out that the design-implement-test-debug loop includes necessarily slow implement-test-debug steps that cannot, at least in the foreseeable future, be reduced by faster computation. On the other hand, perhaps in the unforeseeable future fast and perfect computational simulation could eliminate those slow implement-test-debug steps? You be the judge. ;-)

Week 2:

Tue., 27 Sep:

Lecture: Intro to State Space Search; Uninformed Search.

Read in Advance: Textbook Chapter 3.1-3.4.

Lecture slides (two parts):

(1) Introduction to State Space Search [PDF; PPT]; and

(2) Uninformed Search [PDF; PPT].

Thu., 29 Sep:

Lecture: Heuristic Search.

Read in advance: Textbook Chapter 3.5-3.7.

Lecture slides: Heuristic Search [PDF; PPT].

Fri., 30 Sep:

Discussion Section (required): Review material for this week.

Search [PDF; PPT]

Week 2 Optional Ungraded Homework:

Homework #1; answer key.

Week 2 Optional Mid-class Video Breaks:

Optional Mid-class Video Breaks for Tue.

Interesting search algorithm visualization web page (interactive demo).

Boston Dynamics Big Dog (new video March 2008)

Optional Mid-class Video Breaks for Thu.

Infinite Mario AI - Long Level

An attempt at a Mario AI using the A* path-finding algorithm.

“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/.

Week 2 Optional Cultural Interest:

A* Search in Interplanetary Trajectory Design, courtesy of Eric Trumbauer, former CS-271 student and Aero/Astro PhD student.

Dr. Trumbauer 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 (JMB cover article) (read full paper).

Prof. Lathrop comments, “The A* algorithm found and proved many global optimum solutions for the difficult and important real-world problem of protein structure prediction from sequence within two hours in search spaces sizes as large as 10^20 to 10^30, which are search space sizes totally and completely inaccessible to blind search methods.”

Week 3:

Tue., 4 Oct:

Lecture: 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].

Thu., 6 Oct:

Quiz #1 (answer key here).

Lecture: start Games/Adversarial Search.

Read in advance: Textbook Chapter 5.1, 5.2, 5.4.

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

Fri., 7 Oct:

Discussion Section (required): Review material for this week.

Heuristic Search and Local Search [PDF].

Sun., 9 Oct, 11:59pm: Project “Minimal AI” due. Tournament results are available here.

Week 3 Optional Ungraded Homework:

Homework #2; answer key.

Week 3 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

“Boxcar 2D (show initially at the start of class, again at the mid-class video break, and finally again at the end of class)”

The program learns to build a car using a genetic algorithm. If you let this program run for a long time (>> 30 generations), you will see that eventually it produces cars well suited to the terrain.

This outcome illustrates a general theme of genetic algorithms: very, very slow; but, eventually, good performance. After all, it took ~3.6 billion years to evolve humans from bacteria (http://en.wikipedia.org/wiki/Timeline_of_evolutionary_history_of_life).

Please note that this eventual good performance of genetic algorithms is conditional upon a representation that allows good solutions to sub-problems to be combined simply, by cross-over, into a globally good solution; if the vector position of the features is completely randomized within the chromosome, any such good performance is lost.

Optional Mid-class Non-video Break for Thu.

(Read the link aloud to the class at the mid-class break.)

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 onto a postcard.

Week 3 Optional Cultural Interest:

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

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

Minton, et. al., 1990, AAAI "Classic Paper" Award recipient in 2008.

How to solve the 1 Million Queens problem with local search.

Week 4:

Tue., 11 Oct.:

Lecture: finish Games/Adversarial Search.

Read in advance: Textbook Chapter 5.3. (Optional: Chapter 5.5 and beyond.)

Lecture slides: Games/Adversarial Search/Alpha-Beta Pruning [PDF; PPT].

Thu., 13 Oct:

Lecture: start Constraint Satisfaction.

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

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

Fri., 14 Oct:

Discussion Section (required): Review material for this week.

Search [PDF; PPT]

Week 4 Optional Ungraded Homework:

Homework #3; answer key.

Week 4 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

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

Optional Mid-class Video Break for Thu.

Google Goggles

Week 4 Optional Cultural Interest:

RoboCup Home Page.

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.

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).

Related URL: “Everything Monte Carlo Tree Search” website.

Week 5:

Tue., 18 Oct.:

Lecture: finish Constraint Satisfaction.

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

Lecture slides: Constraint Propagation [PDF; PPT].

Thu., 20 Oct:

Quiz #2 (answer key here).

Lecture: start Propositional Logic.

Read in advance: Textbook Chapter 7.1-7.4.

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

Fri., 21 Oct:

Discussion Section (required): Review material for this week.

Sun., 23 Oct, 11:59pm: Project “First-Draft AI” due. Tournament results are available here.

Week 5 Optional Ungraded Homework:

Homework #4; answer key .

Week 5 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

“Flexible Muscle Based Locomotion for Bipedal Creatures” --- video

Optional Mid-class Video Break for Thu.

Audi Piloted Parking (Audi's self-parking car)

[DARPA] Alice's Crash (spectator view)

Week 5 Optional Cultural Interest:

“Flexible Muscle-Based Locomotion for Bipedal Creatures” --- paper.

Autonomous car - Wikipedia, the free encyclopedia

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

DARPA Urban Challenge Highlights

[DARPA] Alice's Crash (road-finding camera) [different view of Alice's Crash (spectator view); long]

[DARPA] Team Oshkosh attempts forced Entry to Main Exchange

DARPA Urban Challenge: Ga Tech hits curb

DARPA Urban Challenge - Sting Racing crash

DARPA Urban Challenge Crash Cornell MIT

DARPA Urban Challenge - robot car wreck [different view of above]

View the crashes above, then view these links with interest tempered by great skepticism:

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

Week 6:

Tue., 25 Oct.:

Lecture: finish Propositional Logic.

Read in advance: Textbook Chapter 7.5 (optional: 7.6-7.8).

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

Thu., 27 Oct.:

Lecture: Review for Mid-term Exam.

Read in advance: All of the above.

Lecture slides: Review [PDF; PPT].

Fri., 28 Oct.:

Discussion Section (required): Propositional Logic [PDF].

General Review and Questions for Mid-term Exam.

Sun., 30 Oct, 11:59pm: Project “Alpha-beta AI” due. Tournament results are available here.

Week 6 Optional Ungraded Homework:

Homework #5; answer key.

Week 6 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

“High-Speed Robot Hand”

“Janken (rock-paper-scissors) Robot with 100% winning rate”

“CubeStormer II”

Optional Mid-class Video Break for Thu.

“Snake Robot Climbs a Tree”

“Asterisk - Omni-directional Insect Robot Picks Up Prey #DigInfo”

“Freaky AI robot, taken from Nova science now”

Week 6 Optional Cultural Interest:

hitchBOT

hitchBOT FaceBook

hitchBOT Instagram

Hitting the road: Hitchbot begins cross-Canada journey

“Canada's hitchBOT travels 4,000 miles to test human-robot bonds --- LA Times.”

HitchBOT, the hitchhiking robot, gets beheaded in Philadelphia

Week 7:

Tue., 1 Nov (No Project Questions --- Full Time to Mid-term Exam):

Mid-term Exam (answer key here).

Read in advance: All of the above.

Thu., 3 Nov:

Lecture: start First Order Logic

Read in advance: Textbook Chapter 8.1-8.5.

Lecture slides: First Order Logic Syntax [PDF; PPT].

Fri., 4 Nov:

Discussion Section (required): Review material for this week.

Week 7 Optional Mid-class Video Breaks:

No Mid-class Video Break for Tue. due to Mid-term Exam.

Optional Mid-class Non-Video Break for Thu.

(Read the link aloud to the class at the mid-class break.)

“Evolution” by R. H. Lathrop.

Week 7 Optional Cultural Interest:

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

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 home page.

Cyc - Wikipedia, the free encyclopedia.

Week 8:

Tue., 8 Nov:

Lecture: finish First Order Logic; Knowledge Representation.

Review Chapter 8.3-8.5

Read in advance: Textbook Chapter 9.1-9.2 (optional 9.5).

Lecture slides (two parts):

(1) First Order Logic Semantics [PDF; PPT]; and

(2) First Order Logic Knowledge Representation [PDF; PPT].

Thu., 10 Nov:

Lecture: Probability, Uncertainty, Bayesian Networks.

Read in advance: Textbook Chapters 13, 14.1-14.5.

Lecture slides [PDF; PPT]:

Reasoning Under Uncertainty.

Bayesian Networks.

Fri., 11 Nov:

Discussion Section (required): Review material for this week.

Sun., 13 Nov, 11:59pm: Project “IDS/Sorting AI” due. Tournament results are available here.

Week 8 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

“Quadrocopter Pole Acrobatics”

“Nano Quadcopter Robots swarm video flying drones”

Optional Mid-class Video Break for Thu.

The Stanford Autonomous Helicopter performing an aerobatic airshow under computer control:

“Stanford Autonomous Helicopter - Airshow #1”

“Stanford Autonomous Helicopter - Airshow #2 Redux”

Week 8 optional cultural enrichment

Video of Judea Pearl’s 2011 Turing Award lecture.

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

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

Week 9:

Tue., 15 Nov:

Quiz #3 (answer key here).

Lecture: start Learning from Examples.

Read in advance: Textbook Chapter 18.1-18.4.

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

Thu., 17 Nov:

Lecture: finish Learning from Examples.

Read in advance: Textbook Chapter 18.5-18.12, 20.1-20.2.

Lecture slides:

Learning Classifiers [PDF; PPT].

Fri., 18 Nov:

Discussion Section (required): Review material for this week.

Week 9 Optional Ungraded Homework:

Homework #6; answer key .

Week 9 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

“10 Incredible Micro-Robots”

“A Swarm of One Thousand Robots”

Optional Mid-class Video Break for Thu.

Amazing Bike Riding Robot!

Honda's robot ASIMO

Cheetah Robot runs 28.3 mph; a bit faster than Usain Bolt

Week 9 optional cultural enrichment

“Machine learning” - Wikipedia, the free encyclopedia

“Data mining” - Wikipedia, the free encyclopedia

Proof that Decision Tree information gain is always non-negative (problem 3, pp. 4-5).

Viola & Jones, Learning, Boosting, Vision [PDF; PPT] (The slide show describes a machine learning system that learns to recognize scale-independent human faces in images at video streaming rates. For more information, read the two papers immediately below)

Reading: Viola & Jones, 2004, “Robust Real-Time Face Detection”

Reading: Freund & Schapire, 1999, “A Short Introduction to Boosting”

Week 10:

Tue., 22 Nov:

Lecture: Clustering (unsupervised learning) and Regression (statistical numeric learning).

Read in advance: Textbook Chapters 18.6.1-2, 20.3.1.

Lecture slides (two parts):

Clustering (Unsupervised Learning) [PDF; PPT].

Linear Regression [PDF; PPT].

Thu., 24 Nov:

Happy Thanksgiving Holiday!

Rejoice, give thanks.

Fri., 25 Nov:

Happy Thanksgiving Holiday!

Rejoice, give thanks.

Week 10 Optional Mid-class Video Breaks:

Optional Mid-class Video Break for Tue.

“Speech Recognition Breakthrough for the Spoken, Translated Word”

No Mid-class Video Break for Thu., due to Thanksgiving Holiday.

Week 10 Optional Cultural Enrichment:

As always, view self-promotional materials with a great deal of skepticism. It is easy to make a computer that can program itself (for better or worse --- often worse). It is hard to make a computer that can program itself “for better, correctly and intelligently.” You be the judge.

“Google reveals it is developing a computer so smart it can program ITSELF.”

As always, view self-promotional materials with a great deal of skepticism. Consider the remarks directly above as you view the links below. You be the judge.

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

Week 11:

Tue., 29 Nov:

Quiz #4 (answer key here).

Lecture: Special Topics Lecture

(1) Special topics request from students;

(2) TA or Prof present their own research;

(3) Catch-up lecture if needed;

(4) Any other use desired.

Thu., 1 Dec:

Lecture: Review for Final Exam.

Read in advance: All of the above.

Lecture slides: Review [PDF; PPT].

Fri., 2 Dec:

Discussion Section (required): Review for Final Exam.

Sun., 4 Dec: Project “Final AI” due. Tournament results are available here.

Week 11 No Optional Mid-class Video Breaks Due to End of Quarter

Week 11 No Optional Cultural Interest Due to End of Quarter

Final Exam:

Tue., 6 Dec., 4:00 - 6:00 p.m. (answer key here).

Project:

Your project is to code a “Connect-K” agent.

Project Coding Shells:

Student resources are available here, including:

v General documentation and background info.

v A C++ shell.

v A Java shell.

v A Python shell.

v A tournament shell.

We will fix any further problems and issue new shells as necessary. Please watch for and download any new shells, then discard your old shells. You can keep any “smart” code you have written, just put it into the new shells.

“Dumb” coding shells are available in C++, Java, and Python. You must write the “smarts.” To help you get started, we also have released the source code to “RandomAI,” and the executables to RandomAI, PoorAI, AverageAI, and GoodAI (they go by different names in different shells). Your final AI will compete in a tournament against all your classmates for extra credit bonus points (the top 10% will get 10 Bonus points, the second 10% will get 9, the third 10% will get 8, and so on). This is a solo or pair project and you must do all of it all by yourself or with one partner.

As noted in lecture, all of my CS-171 project shells were written by former CS-171 students (grade of A- or better required) who wanted to go further and do something creative and interesting. The original Java shell was written by XXXX. The Python shell was written by XXXX. The C++ shell was written by XXXX, then revised by XXXX. The tournament shell was written by XXXX.

Project Deadlines and Regulations:

· Project deadlines are given above in the “Syllabus Overview and Important Dates” section.

· Your EEE DropBox submission must be a single “zipped” file named “yourLastName_yourUCINumericID_yourTeamName.” NO SPACES OR ANY OTHER SPECIAL UNIX CHARACTER in yourTeamName. Please restrict your TeamName to characters, digits, hyphen, and underscore, or else you may lose points.

· It should have three subdirectories: src, bin, & doc; for source, executable, and documents (‘doc’ must contain your Project Report).

· Your main AI file must contain the string “AI” and no other file may contain the string “AI” (case is ignored, i.e., “ai” == “AI”).

You will lose 10% of your project score for each day (or part thereof) that your project is late for any deadline. Please submit your project early, well ahead of the deadline, and avoid the last-minute rush. If system problems, web congestion, or other unavoidable Internet delays make your project late, it is still late and will be penalized.

Study Guides --- Previous CS-171 Tests:

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.

Please note that some of the very old tests below reflect different textbooks that may define some things differently than does your current textbook. In case of conflict, your current textbook is deemed correct and will prevail. Some of your visualization systems may not display the red PDF overlays used to correct errors in very old tests. For example, in problems #2a, #2c, #3a, and #3b on Quiz #2 from SQ’2004, the PDF overlay is invisible on a Mac (iPad), and possibly on some other systems or printers. The PDF overlays just do not seem to work as advertised (sorry!!), but this problem seems only to afflict very old tests (i.e., from over a decade ago). If you are confused by any of the answers below, please bring your questions to the TA in Discussion Section (required). If you find a genuine error anywhere, please send me email and you will receive a Bonus Point if correct.

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.

Summer Session I 2016:

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.

Winter Quarter 2016:

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 2015:

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.

Winter Quarter 2015:

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 2014:

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.

Winter Quarter 2014:

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 2013:

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 2012:

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

Winter Quarter 2012:

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 2011:

Quiz #1 and key

Quiz #2 and key

Quiz #3 and key

Quiz #4 and key

Quiz #5 and key

Mid-term Exam and key

Final Exam and key

Spring Quarter 2004:

Quiz #1 key

Quiz #2 key

The correct answer to Quiz #2 (2a) is A B D E C G.

The correct answer to Quiz #2 (2c) is A; A B C G.

The correct answer to Quiz #2 (3a) is N.

The correct answer to Quiz #2 (3b) is N.

These emendations to Quiz #2 have been corrected by overlays to the old PDF files, but apparently those corrections may not be not visible on some systems (MAC/iPAD?) or when printed on some printers (?). Please be warned.

Spring Quarter 2000:

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.

Website for American Association for Artificial Intelligence (AAAI).

AAAI page of AI Resources.

AAAI page of AI Topics.

AAAI AI in the News.

AAAI Digital Library of more than 10,000 AI technical papers.

AAAI AI Magazine.

AAAI Author Kit.

AAAI Classic Papers.

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 Academic Senate Policy On Academic Integrity 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.

CompSci (CS) 171 — Introduction to Artificial Intelligence — Fall Quarter 2016

Jump to section:

Current Announcements:

Time, Place:

Lecture:

Place: ELH 100 (Engineering Lecture Hall; Building 305 on the UCI campus map) Time: Tuesday/Thursday, 3:30- 4:50pm

Coding Project Help:

· “Project Questions and Answers” in ELH-100, at the beginning of every lecture throughout the quarter, for brief immediate answers to project questions.

· “Project Clinic” in DBH-3013, every Monday 4-5p and Wednesday 5-6pm throughout the quarter, for in-depth personal help with project coding issues.

Discussion section is REQUIRED and roll will be taken.

Discussion section 1:

Place: ICS 174 (Building 302 on the UCI campus map) Time: Friday, 1:00- 1:50pm

Discussion section 2:

Place: ICS 174 (Building 302 on the UCI campus map) Time: Friday, 2:00- 2:50pm

Discussion section 3:

Place: ICS 174 (Building 302 on the UCI campus map) Time: Friday, 3:00- 3:50pm

Discussion section 4:

Place: ICS 174 (Building 302 on the UCI campus map) Time: Friday, 4:00- 4:50pm

Teaching Staff:

Instructor:

Alex Ihler Office hours: TBA, or anytime by appointment, in DBH room 4066.

Email: aihler@uci.edu or ihler@ics.uci.edu

(If you send email, please put “CS-171” somewhere in the Subject line.)

Coding Project Tournament Director:

Toluwanimi Salako

Office hours: “Project Clinic” Mon. 4-5pm/Wed. 5-6pm in DBH-3013.

This time will be a “Project Clinic” where you can get answers to project questions and help with project problems. Tolu also will attend the first part of each class to answer project questions.

Email: tsalako@uci.edu

(If you send email, please put “CS-171” somewhere in the Subject line.)

TAs:

TBA #1

Office hours: TBA, or anytime by appointment, in TBA.

Email: TBA

(If you send email, please put “CS-171” somewhere in the Subject line.)

TBA #2

Office hours: TBA, or anytime by appointment, in TBA.

Email: TBA

(If you send email, please put “CS-171” somewhere in the Subject line.)

[... repeat TBA TAs as needed ...]

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.

Class Setup:

There will be an AI coding project (see Project section below). This is an individual or pair project, i.e., you must do it entirely by yourself or form a team of two people.

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).

Please start your AI coding project earlier than you believe necessary, i.e., immediately; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

Textbook

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

Grading:

Your grade will be based on Discussion Section participation (10%), a coding project (20%), the four quizzes (20%), a mid-term exam (25%), and a final exam (25%). Homework is assigned but ungraded.

·

· The AI coding project will be a “Connect-K” AI agent. “Dumb” coding shells are available in C++, Java, and Python. You must write the “smarts.” This is a solo or pair project and you must do all of it all by yourself or with a single team-mate.

You are already behind schedule. Start coding now. More details below in the Project section. You will lose 10% of your Project grade for every day or fraction thereof your project submission is late.

Your “Final AI” will be entered into a tournament against all of your classmate’s Final AIs. The top 10% will have their Project score increased by 10% (= 2% of total grade), the second 10% by 9%, the third 10% by 8%, and so on.

·

·

· The Mid-term exam will be given in class on Tuesday, 1 Nov., and is closed-book, closed-notes. It is not possible to make-up a missed Mid-term exam.

·

· The Final exam will be given in class on Tuesday, 6 Dec., 4-6pm, and is closed-book, closed-notes. The Final exam will cover all course material from the entire quarter, with emphasis on the second half. It is not possible to make-up a missed Final exam.

I honor all requests made by the UCI Disability Services Center.

Also, I make exceptions for:

* genuine medical conditions (I require a signed note from your doctor on official letterhead),

* births/deaths in the family (I require a copy of the birth/death certificate),

* jury duty or other court proceedings (I require a copy of your jury service papers or other official court documents), or

* field maneuvers of the US military or National Guard (I require a copy of your official orders).

·

· 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.

Your Bonus Points, if any, should be visible to you in EEE GradeBook. If for some reason you have been awarded a Bonus Point, but you did not get a notification from me or it did not appear in EEE GradeBook, please do not hesitate to send an email message to me as a reminder.

Study Habits:

Please do not ever fall behind in the class material; instead, study frequently and diligently. Please begin your AI coding project earlier than you believe necessary; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

Please work and understand all past quizzes and exams; they are important guides about the performance that will be expected from you now. Please work and understand all the optional homework.

Please OVERSTUDY!!

AI at UCI/ICS:

UCI/ICS AI Faculty and Their Suggested Papers for CS-171

Syllabus:

Syllabus Overview and Important Dates

(Please read textbook and lecture notes before AND after each lecture)

(Please note that Coding Project Clinics are 4-5pm Mondays, and 5-6pm Wednesdays, in order to accommodate disparate conflicting schedules.)

Week

Place: ELH 100 (Engineering Lecture Hall; Building 305 on the UCI campus map)
Time: Tuesday/Thursday, 3:30- 4:50pm

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 1:00- 1:50pm

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 2:00- 2:50pm

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 3:00- 3:50pm

Place: ICS 174 (Building 302 on the UCI campus map)
Time: Friday, 4:00- 4:50pm

Alex Ihler
Office hours: TBA, or anytime by appointment, in DBH room 4066.

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.