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


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.
  • Instructor: Kalev Kask
    • Email: kkask@uci.edu; when sending email, put CS271 in the subject line
    • Office hours: TBD
  • TA: Neftali Watkinson
  • Reader: Zhengli Zhao
  • 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 (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.
    • Game playing: Adversarial 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