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


CompSci 271: Introduction to Artificial Intelligence, Fall 2013


Course Outline

  • When: Monday & Wednesday, 11:00 a.m. - 12:20 p.m.
  • Where: DBH 1300 UCI campus map
  • Course Code: 35350
  • Discussion section : Wed 12:30-1:20 ET 202
    • 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: DBH 4241; Mon 1-2pm or by appointment
  • Reader: Seyed Hejrati
  • 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 1-11 in the course book.

Assignments:

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

Course-Grade:

Homeworks plus project will account for 50% 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 as well as making a presentation to the whole class (note the last two weeks are for project presentations). Due to the large number of students enrolled, each project will be a team project (2-3 stundents per team).

Project page is here

Syllabus:

Subject to changes

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

  • Problem solving, the search space approach, state space graph
09-30 RN
Ch. 1, 2

Ch. 3		 Lecture 1

Lecture 2 		Set 1

Set 2
Week 2
  • Uninformed search: Breadth-First, Uniform cost, Depth-First, Iterative Deepening

  • Informed heuristic search: Best-First, Greedy search, A*, Branch and Bound, AND/OR search
10-07 RN
Ch. 3, 4 		Lecture 3



Lecture 4



Set 3
Week 3
  • Properties of A*: Iterative Deepening A*, generating heuristics automatically. Beyond classical search.

  • Game playing.
10-14 RN
Ch. 4, 5		 Lecture 5



Lecture 6



Set 4
Week 4
  • Game playing (cont.).
  • Constraint satisfaction problems
10-21 RN
Ch. 5, 6		 Lecture 7

Lecture 8

Set 5
Week 5
  • Constraint satisfaction problems (cont.)
  • Knowledge and Reasoning:
    Logical agents.

10-28 RN
Ch. 6, 7		 Lecture 9

Lecture 10

Set 6
Week 6
  • Knowledge Representation:
    Propositional inference, First-order logic.
11-04 RN
Ch. 7, 8		 Lecture 11

Lecture 12 		Set 7

Set 8
Week 7
  • Knowledge representation (cont.):
    First-order Logic.

11-11 RN
Ch. 9

Lecture 13

Week 8
  • Classical Planning: Planning as state-space search, Planning graphs, STRIP, Planning as satisfiability.

11-18 RN
Ch. 9, 10 		Lecture 14


Lecture 15 		Set 9

Week 9
  • Planning (cont.): Planning systems, STRIP, search-based, and propositional-based, Planning and acting in the real world
  • Final
11-25 RN
Ch. 10, 11 		Lecture 16 Final Study Guide
Week 10
  • Project Presentations
12-02
Week 11
  • Project Presentations
12-09


Resources on the Internet

Essays and Papers