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


CompSci 271: Introduction to Artificial Intelligence, Fall 2014


Course Outline

  • When: Tuesday & Thursday, 11:00 a.m. - 12:20 p.m.
  • Where: HG 1800 UCI campus map
  • Course Code: 35360
  • Discussion section : Wed 4-5pm DBH 1600 and Fri 2:00-2:50 SSL 290.
    • 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 4214 Fri 1-2pm.
  • TA: Edwin Vargas
  • Reader: Nadia Ahmed
  • 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.

Course-Grade:

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 (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.

09-29 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

10-06 RN
Ch. 3




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

10-13 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.
  • Game playing: Adversarial search.
 10-20 RN
Ch. 3, 4







RN
Ch. 5		 Lecture 6








Lecture 7








Set 4
Week 5
  • Game playing cont.
  • Constraint satisfaction problems: Formulation, Search.
 10-27

RN
Ch. 6		 Lecture 8

Lecture 9

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

  • Knowledge and Reasoning:
    Logical agents, Propositional inference.
 11-03




RN
Ch. 7		 Lecture 10




Lecture 11




Set 6
Week 7
  • No class 11-11 (holiday)
  • Knowledge and Reasoning:
    Propositional logic.

11-10


RN
Ch. 7


Lecture 12


Week 8
  • Propositional logic : inference.
  • Knowledge representation:
    First-order Logic.

11-17


RN
Ch. 8, 9 		Lecture 13


Lecture 14


Set 7
Week 9
  • First-order Logic cont.
  • No class 11-27 (holiday)
 11-24 Lecture 15 Set 8
Week 10
  • Classical Planning: Planning systems, propositional-based, Planning graphs, Planning as satisfiability and state-space search, STRIPs planning.
12-01 RN
Ch. 10, 11 		Lecture 16 Set 9



Week 11
  • Final review
12-08 Final Study Guide


Project Repost Guidelines
Week 12
  • Final : 12-16 (10:30-12:30)
12-15


Resources on the Internet

Essays and Papers