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


CompSci 271: Introduction to Artificial Intelligence, Fall 2015


Course Outline

  • When: Tuesday & Thursday, 3:30 - 4:50p
  • Where: ICS 174 UCI campus map
  • Course Code: 35360
  • Discussion section : Fri 9:00-10:50 ICS 174.
    • 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
  • Reader: Junkyu Lee
  • 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).
  • Projects can be of the following types
    • Article-centered project.

      A project can be centered on 1-2 primary articles selected from a recent AI conference(IJCAI, AAAI, UAI, CP, SoCS, etc.)/journal(e.g. AIJ, JAIR, JMLR, etc.). The idea is that the paper will open a window to a subject that the students read and explore. You may need to read earlier papers for background and information (that appear in the citations of the primary papers). Students may investigate the validity of paper by running code developed on some benchmarks whenever relevant. The team will write a project report to be handed in at the end of the course. The project report should include a description and evaluation of the paper and any creative independent work carried out by the team.
    • Programming project.

      A project can consist of picking a particular problem class and task (e.g. solving Constraint Satisfaction Problems), and writing a computer program to solve problem instances of this class. There are repositories of benchmarks that students can get problem instances, for example
    • Your own project idea, on the topic of AI (e.g., implementing an algorithm and applying to some domain). Students need to obtain approval before proceeding.
  • Each team needs to submit a written report (one report per team) at the end of the course (exact date TDB).
  • 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.

09-24 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

09-28 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*.

10-05 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.
 10-12 RN
Ch. 3, 4







RN
Ch. 5		 Lecture 6








Lecture 7








Set 4
Week 5
  • Game playing cont.
  • Constraint satisfaction problems: Formulation, Search.
 10-19

RN
Ch. 6		 Lecture 8

Lecture 9

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

  • Knowledge and Reasoning:
    Logical agents, Propositional inference.
 10-26




RN
Ch. 7		 Lecture 10




Lecture 11




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

  • Knowledge representation:
    First-order Logic.
11-02


RN
Ch. 7
Lecture 12



Lecture 13




Set 7
Week 8
  • First-order Logic cont.

  • First-order Logic cont.
 11-09
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.
11-16


RN
Ch. 10, 11 		Set 9
Week 10
  • Final.
  • No class 11-26 (holiday)
 11-23 Final Study Guide



Week 11
  • Project Presentations
11-30
Week 12
  • Project Presentations
12-07 Project Report Guidelines


Resources on the Internet

Essays and Papers