Dr. Kalev Kask - University of California at Irvine ZOT!

CompSci 271: Introduction to Artificial Intelligence, Fall 2018

Course Outline

  • When: Tuesday & Thursday, 5:00 - 6:20pm
  • Where: ICS 174 UCI campus map
  • Course Code: 34900
  • Discussion section : Wed 9:00-9:50 and 10:00-10:50 PCB 1200.
    • Optional. It purpose is to explore topics in more depth, to work on concrete examples, or to get help in understanding difficult parts of the material.
  • Instructor: Kalev Kask
    • Email: kkask@uci.edu; when sending email, put CS271 in the subject line
    • Office hours: TBD
  • TA: Reza Asadi
  • Reader: Ananya Ananya
  • Textbook

Course Overview

The goal of this class is to familiarize you with the basic principles of Artificial Intelligence. Topics covered Include: Heuristic search, Adversarial search, Constraint Satisfaction Problems, Knowledge representation, (Classical) Planning. We will cover much of the content of chapters 1-10 in the course book.


We will assume basic familiarity with the concepts of algorithms and data structures, and some programming experience.


  • Homeworks 20%
  • Project 30%
  • Final 50%


There will be a few homework-assignments (one per topic, mostly weekly), a project, and a final exam. For homework/project submissions, we will use a course gradescope page.


There is a discussion section on Wed 9:00-9:50 and 10:00-10:50 in PCB 1200. This is where you can discuss course material, and discuss homework/project related issues/questions.

We will use a course Piazza page for questions and discussion. Please post your questions there; you can post privately if you prefer, or if (for example) your question needs to reveal your solution to a homework problem. I prefer to use Piazza for all class contact, since it enables responses by either myself, the TA, or fellow students (if public), which should get you answers more quickly.

Note: when posting privately, please post to "Instructors" (which includes the instructor & TAs).


You will be required to do a project. This includes submitting a written report at the end of the quarter :
  • Due to the large number of students enrolled, each project will be a team project (ideally 3 students per team).
  • Project involves writing a computer program utilizing AI methods/techniques/concepts (learned in this course) to solve a problem.
    • Boolean (propositional) satisfiability :
      • input is a set of clauses in CNF form, e.g. "(3,-5,6),(-9,2,1)" means (X3 ∨ ¬ X5 ∨ X6) ∧ (¬ X9 ∨ X2 ∨ X1)"
      • output is a vector of 0's and 1's of length n that satisfies the formula, or NULL if no such assignment exists, e.g. "(1,0,1,0,0,0,0,0,0)"
    • Game of Othello :
      • initially, the input is a vector of 64 symbols {0,W,B} defining the starting board
      • the computer moves first
      • output after each computers move is a pair (i,j) that defines the computers move on the 8x8 board
      • after computers move, it should expect as input a pair (x,y) that defines opposing players move
      • until the game is over (current player cannot move).
    • (Classic) Sokoban,
    • input is 5 lines defining the board :

      • sixeH sizeV, e.g. "3 5"
      • nWallSquares a list of coordinates of wall squares, e.g. "12 1 1 1 2 1 3 2 1 2 3 3 1 3 3 4 1 4 3 5 1 5 2 5 3"
      • nBoxes a list of coordinates of boxes, e.g. "1 3 2"
      • nStorageLocations a list of coordinates of storage locations, e.g. "1 4 2"
      • playes initial locatin x and y, e.g. "2 2"
      output is a single line, beginning with nMoves followed by a sequence of letters (U,D,L,R) indicating direction of the move, e.g. "1 D".

    • Mastermind : input is (a) number of colors and positions, (b) a response to each guess by the computer; output is a series of guesses, each consisting of a color per position.
  • Each team needs to submit a written report (one report per team) at the end of the course.
  • There will be a competition between teams solving the same problem; team with best performing program will get bonus points.
  • Teams should be formed and project selection finalized by early Nov.
  • Further details TBA.


Subject to changes

Week Topic Date   Reading    Lecture      Slides Homework  
Week 1
  • Introduction, History, Intelligent agents.

09-27 RN
Ch. 1, 2

Set 1

Week 2
  • Problem solving, state space, search space
  • Uninformed search: Breadth-First, Uniform cost, Depth-First, Iterative Deepening


Set 2 Homework 1
Week 3
  • Informed heuristic search: Best-First, Greedy search, A*.
  • Informed heuristic search cont. Properties of A*. Branch and Bound, Iterative Deepening A*, generating heuristics automatically. Beyond classical search, AND/OR search.



Set 3 Homework 2
Week 4
  • Game playing: Adversarial search. Game tree, MINIMAX algorithm, evaluation function.
  • Game playing: Alpha/Beta pruning, stochastic games.

Ch. 4

Ch. 5

Set 4
Homework 3
Week 5
  • Game playing cont.
  • Constraint satisfaction problems: Formulation, Search.


Ch. 6

Set 5
Homework 4
Week 6
  • Constraint satisfaction problems cont.: Inference.

  • Knowledge and Reasoning:
    Logical agents, Propositional inference.


Ch. 7

Set 6
Week 7
  • Knowledge and Reasoning:
    Propositional logic : inference.

  • Knowledge representation:
    First-order Logic.


Ch. 8

Set 7
Homework 5
Week 8
  • First-order Logic cont.
  • First-order Logic cont.


Ch. 9

Set 8

Homework 6
Week 9
  • Classical Planning: Planning systems, propositional-based, STRIPs planning.
  • No class 11-22 (holiday)
11-20 RN
Ch. 10
Set 9
Week 10
  • Classical Planning: Planning graphs, Planning as satisfiability and state-space search.
  • Extra : Selected topics from Machine Learning.

Final Study Guide

Homework 7
Week 11
  • Extra : Selected topics from Machine Learning.
  • Extra : Selected topics from Machine Learning.

Project Report Guidelines
Week 12
  • Project reports due Sun 12/09 midnight
  • Final Thu 12/13 4:00-6:00pm

Resources on the Internet

Essays and Papers