CPSC 322 - Introduction to Artificial Intelligence (Term 1,  Summer 2017)
Overview  Grades Textbook Schedule  


 

Overview

Course Description:  This course provides an introduction to the field of artificial intelligence.  The major topics covered will include reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.

Grades

Grading Scheme: Evaluation will be based on a set of  assignments, a midterm, and an exam. Important: you must pass the final in order to pass the course. The instructor reserves the right to adjust this grading scheme during the term, if necessary.

If your grade improves substantially from the midterm to the final, defined as a final exam grade that is at least 20% higher than the midterm grade, then the following grade breakdown will be used instead.

The assignment grade will be computed by adding up the number of points you get across all assignments, dividing this number by the number of possible points, and multiplying by 20.  Assignments will not be graded out of the same number of points; this means that they will not be weighted equally.

 

Late Assignments: Assignments are to be handed in BEFORE the start of lecture on the due date. However, every student is allotted four "late days", which allow assignments to be handed in late without penalty on three days or parts of days during the term.  The purpose of late days is to allow students the flexibility to manage unexpected obstacles to coursework that arise during the course of the term, such as travel, moderate illness, conflicts with other courses, extracurricular obligations, job interviews, etc.  Thus, additional late days will NOT be granted except under truly exceptional circumstances.  If an assignment is submitted late and a student has used up all of her/his late days, 20% will be deducted for every day the assignment is late. (E.g., an assignment 2 days late and graded out of 100 points will be awarded a maximum of 60 points.)

How late does something have to be to use up a late day? A day is defined as a 24-hour block of time beginning at 1 PM on the day an assignment is due.  To use a late day, write the number of late days claimed on the first page of your assignment

 Examples:

Assignments can be handed in electronically using handin; this is the only way to hand in late assignments over a weekend. Written work can also be put in Giuseppe's mailbox in the main CS office (room 201); ask the secretary to time-stamp it.

Missing Deadlines or Exams: In truly exceptional circumstances, when accompanied by a note from Student Health Services or a Department Advisor, the following arrangements will be made.

Academic Conduct: Submitting the work of another person as your own (i.e. plagiarism) constitutes academic misconduct, as does communication with others (either as donor or recipient) in ways other than those permitted for homework and exams. Such actions will not be tolerated. Specific rules for this course will be clearly described on each assignment.

Violations of the rules constitute very serious academic misconduct, and they are subject to penalties ranging from a grade of zero on the current and *all* the previous assignments to indefinite suspension from the University. More information on procedures and penalties can be found in the Department's Policy on Plagiarism and collaboration and in  UBC regulations on student discipline . If you are in any doubt about the interpretation of any of these rules, consult the instructor or a TA!


Textbook

Selected Chapters of Artificial Intelligence: foundations of computational agents by David Poole and Alan Mackworth, Cambridge University Press, 2010 - Complete book available online Although this text will be our main reference for the class, it must be stressed that you will need to know all the material covered in class, whether or not it is included in the readings or available on-line.  Likewise, you are responsible for all the material in assigned readings, whether or not it is covered in class. If you'd like to refer to an alternate text, I recommend Russell and Norvig's Artificial Intelligence: A Modern Approach (third edition). I've arranged for a copy to be put on reserve in the CS reading room.

Further readings on topics covered in 322

Schedule

Here is where you can find the course schedule and the PDF files from lectures. These dates will change throughout the term, but this schedule will be kept up to date.  Assignment due dates are provided to give you a rough sense; however, they are also subject to change. I will try to always post the slides in advance (by 11am). After class, I will post the same slides inked with the notes I have added in class.

 

Date Lecture

Book Chp

Notes
Tue, May 16 What is AI? [pdf ] 1.1-1.3  
  Representational Dimensions [pdf ] 1.4,1.5  
  Applications of AI [pdf ] 1.6  
Thur, May 18 Search: Intro [pdf ] 3.1-3.4  
  Search: Uninformed Search, DFS and BFS [pdf   3.5.1, 3.5.2 ex1 ex2 (AIspace)
  Search: IDS, Search with Costs [ pdf ] 3.7.3, 3.5.3 ex3
Tue, May 23 Search: Heuristic Search [pdf ] 3.6 intro Assignment 1 out (see Connect)
  Search: BestFS, A*, optimality,  [pdf ] 3.6.1 ex4 
Search: Branch&Bound, IDA*, Pruning.... .... [ pdf ] 3.7.1, 3.7.4
Thur, May 25 Search: Dynamic Prog. and Recap [ pdf ]    
  CSP Introduction [pdf ] 4.1, 4.2  
CSPs: Search and Consistency [ pdf ] 4.3, 4.4
Tue, May 30 CSPs: Arc Consistency & Domain Splitting [ pdf ] 4.5, 4.6 Assignment 1 due
  CSPs: Local Search [ pdf ] 4.8, 4.10 (intros)  
  CSPs: Stochastic Local Search  [ pdf ] 4.8.1 - 4.8.3  
Thur, Jun 1 CSPs: SLS variants (Sim. Annealing + Pop. based) [ pdf ] 4.9 Assignment 2 out (see Connect)
  Planning: Representations and Forward Search [ pdf ] 8.1 - 8.2 Summary of Planning Competition 2008 (see slides 15-18 for participating planners, and slide 24 for domains)
  Planning: Heuristics and CSP Planning [ pdf ] 8.4 delivery robot STRIPS->CSP available in AI space simpleCommuting.xml  complexCommuting.xml
Tue, Jun 6 Logic: Intro and Syntax [ pdf ] 5.1 - 5.1.1 - 5.2  
  Logic: Semantics and Bottom-Up Proofs [ pdf ] 5.1.2 - 5.2.2  
  Logic: BU Sound and Complete [ pdf ] 5.2.2.1 solution q.3 (load in AIspace)
Thur, Jun 8 Midterm exam (50 mins, ?same room DMP 310 ?)   Assignment 2 due
  Logic: Domain Modeling and Top-Down Proofs [ pdf ] Ex. 5.5,  5.2.2.2  
  Logic: Datalog [ pdf ] 5.2,  12 (basic concepts) ex5.9  exDatalog (load in AIspace)
Tue, Jun 13 Uncertainty: Probability Theory [ pdf ] 6.1, 6.1.1 Assignment3 out (see Connect)
  Uncertainty: Conditional Probability [ pdf ] 6.1.3.1-2  
  Uncertainty: Conditional Independence [ pdf ] 6.2  
Thur, Jun 15 Uncertainty: Belief Networks [ pdf ] 6.3 burglary example (load in AIspace)
  Uncertainty: Belief Nets (indep. compactness, apps) [ pdf ] 6.3-6.3.1 email spam ex. (load in AIspace)
  Uncertainty: BNs inference ( intro Variable Elimination) [ pdf ]    
Tue, Jun 20 Uncertainty: Variable Elimination Example       [ pdf ] 6.4.1 Assignment3 due
Assignment4 out Blackjack.xml 
(see Connect)
  Uncertainty: Temporal Probabilistic Models      [ pdf ] 6.5- 6.5.1  
  Uncertainty: Hidden Markov Models [ pdf ] 6.5.2  
Thur, Jun 22 Decision Theory: Single-Stage Decisions [ pdf ] 9.2 robot example (load in AIspace)
  Decision Theory: Sequential Decisions (policies) [ pdf ] 9.3 umbrella example (load in AIspace) Assignment4 due Mon Dec 2, 1PM. You can drop it at my office (ICICS 105)or by handin.
  Decision Theory: MDPs [pdf] 9.4  
Decision Theory: Finish MDPs [pdf]    
  Decision Theory: VE , Value of Info and Control [pdf]   Assignment4 due
TBD

Final Exam (2.5 hours,  ) Room: