CSE 327 AI Theory and Practice, Spring 2016
Professor Jeff Heflin
TTh 1:10-2:25pm, Sinclair Auditorium
Check here for updates regarding the course.
- 2/8/17 - In Homework #1, exercise #4, the SUCCESSOR-FN is not defined. Assume SUCCESSOR-FN(s) returns a set of <a,s'> pairs, where a is an action that can be performed in s and s' is a state that results if a is performed in s. Also, LEGAL-ACTIONS is just the ACTIONS function as described on p. 67.
This course will provide a general introduction to Artificial Intelligence(AI). We will discuss what AI is, survey some of the major results in the field, and look at a few promising directions. In particular, we will seek answers to questions such as:
Our examination of these problems will focus on various data structures and algorithms that have been proposed as solutions.
- How do you represent and reason with general-purpose knowledge?
- How can a robot or artificial agent formulate a plan to achieve a task?
- How can an agent make good decisions given uncertainty about its environment?
- How can an agent learn in order to improve its behavior or cope with unanticipated situations?
For details about course content, grading, and assignments, see the class syllabus.
Mon. 1-2:30pm, Thr. 10-11:30am and by appointment in Packard Lab 330
Each of the homeworks will be made available here after they are
handed out in class. The online versions of the homework are in PDF format.
Your readings will be listed below as they are assigned. Unless otherwise specified, all readings are from our textbook, Artificial Intelligence: A Modern Approach.
|Read Ch. 1-2.3 (pp. 1-46)||1/28|
|Read Sect. 2.4-2.5 (pp. 46-59)||2/2|
|Read Sect. 3.1-3.4 (pp. 64-91)||2/4|
|Read Sect. 3.5-3.7 (pp. 92-109)||2/9|
Additional Class Materials
- Contains information on course content, grading, assignments, and office
- Supplemental Slides
- This directory contains the slides that I use in class. Note, these slides only cover part of the lecture, and should not be used as a substitute for it.