Dr. Kalev Kask - University of California at Irvine

 CompSci 271: Introduction to Artificial Intelligence, Fall 2016

Course Outline

• When: Tuesday & Thursday, 3:30 - 4:50p
• Where: SH 134 UCI campus map
• Course Code: 35360
• Discussion section : Tue 5:00-6:50 ICS 180.
• 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.
• Email: kkask@uci.edu; when sending email, put CS271 in the subject line
• Office hours: TBD
• TA: Neftali Watkinson
• 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, Reasoning and Planning. We will cover much of the content of chapters 1-14 in the course book.

Assignments:

There will be weekly homework-assignments, a project, and a final.

Homeworks will account for 20% of the grade, project 30% of the grade, final 50% of the grade.

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 (3-4 stundents per team).
• Project involves writing a computer program to solve one of the following four problems :

• N-queens : input is an integer N; output should be a sequence of integers (ranging [1,N]) of length N, containing a position of a queen in each column, left to right.
• (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".

• Sudoku : input is a sequence of 81 interers ranging [0,9], encoding the initial board position, left-to-right and top-down, with 0 for empty squares; output should be a sequence of numbers ranging [1,9].
• 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 (exact date TDB).
• 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 proposals finalized/approved by early Nov at the latest.

• Syllabus:

Subject to changes

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

09-19 RN
Ch. 1, 2
Lecture 1

Set 1

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

09-26 RN
Ch. 3
Lecture 2

Lecture 3
Set 2
Week 3
• Informed heuristic search: Best-First, Greedy search, A*.
• Informed heuristic search cont. Properties of A*.

10-03 RN
Ch. 3
Lecture 4

Lecture 5
Set 3
Week 4
• Informed heuristic search cont. Branch and Bound, Iterative Deepening A*, generating heuristics automatically. Beyond classical search, AND/OR search.
10-10 RN
Ch. 3, 4

RN
Ch. 5
Lecture 6

Lecture 7

Set 4
Week 5
• Game playing cont.
• Constraint satisfaction problems: Formulation, Search.
10-17

RN
Ch. 6
Lecture 8

Lecture 9

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

• Knowledge and Reasoning:
Logical agents, Propositional inference.
10-24

RN
Ch. 7
Lecture 10

Lecture 11

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

• Knowledge representation:
First-order Logic.
10-31

RN
Ch. 7

Lecture 12

Lecture 13

Set 7
Week 8
• First-order Logic cont.
• First-order Logic cont.
11-07
RN
Ch. 8, 9
Lecture 14

Set 8

Week 9
• Classical Planning: Planning systems, propositional-based, STRIPs planning.
• Classical Planning: Planning graphs, Planning as satisfiability and state-space search.
11-14

RN
Ch. 10, 11
Set 9
Week 10
• Final.
• No class 11-24 (holiday)
11-21 Final Study Guide

Week 11
• Project Presentations
11-28
Week 12
• Project Presentations
12-05 Project Report Guidelines

Resources on the Internet

Essays and Papers