 When: Tuesday & Thursday, 5:00  6:20pm
 Where: ICS 174 UCI campus map
 Course Code: 34900
 Discussion section : Wed 9:009:50 and 10:0010:50 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
 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 110 in the course book.
Background:
We will assume basic familiarity with the concepts of algorithms and data structures, and some programming experience.
CourseGrade:
 Homeworks 20%
 Project 30%
 Final 50%
Assignments:
There will be a few homeworkassignments (one per topic, mostly weekly), a project, and a final exam.
For homework/project submissions, we will use a course
gradescope page.
Discussion:
There is a discussion section on Wed 9:009:50 and 10:0010:50 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).
Project:
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.
Syllabus:
Subject to changes
Week 
Topic 
Date 
Reading 
Lecture 
Slides 
Homework 
Week 1 
 Introduction, History, Intelligent agents.

0927 
RN Ch. 1, 2 

Set 1


Week 2 
 Problem solving, state space, search space
 Uninformed search: BreadthFirst, Uniform cost, DepthFirst, Iterative Deepening

1002
1004 
RN Ch.3.13.4 

Set 2

Homework 1

Week 3 
 Informed heuristic search: BestFirst, 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.

1009
1011 
RN Ch.3.53.7 

Set 3

Homework 2

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

1016
1018 
RN Ch. 4
RN Ch. 5 

Set 4

Homework 3

Week 5 
 Game playing cont.
 Constraint satisfaction problems: Formulation, Search.

1023
1025 
RN Ch. 6 

Set 5

Homework 4

Week 6 
 Constraint satisfaction problems cont.: Inference.
 Knowledge and Reasoning:
Logical agents, Propositional inference.

1030
1101 
RN Ch. 7 

Set 6


Week 7 
 Knowledge and Reasoning:
Propositional logic : inference.
 Knowledge representation:
Firstorder Logic.

1106
1108 
RN Ch. 8 

Set 7

Homework 5

Week 8 
 Firstorder Logic cont.
 Firstorder Logic cont.

1113
1115 
RN Ch. 9 

Set 8

Homework 6

Week 9 
 Classical Planning: Planning systems, propositionalbased, STRIPs planning.
 No class 1122 (holiday)

1120 
RN Ch. 10 

Set 9


Week 10 
 Classical Planning: Planning graphs, Planning as satisfiability and statespace search.
 Extra : Selected topics from Machine Learning.

1127
1129 


Final Study Guide

Homework 7

Week 11 
 Extra : Selected topics from Machine Learning.
 Extra : Selected topics from Machine Learning.

1204
1206 


Project Report Guidelines


Week 12 
 Project reports due Sun 12/09 midnight
 Final Thu 12/13 4:006:00pm

1213 




Resources on the Internet
Essays and Papers
