Course Number: CSCI 682

Course Name: Artificial Intelligence in Games

Prerequisite: CSCI 112 (basically one year of programming, although programming is not necessarily required for the course)

Prerequisite by Topic: An understanding of programming and desire to make computers do more

Units: 3

Class Dates and Times: 12:30-1:45 Tuesday/Thursday daily contents(on campus), Offsite/self-paced: daily contents has what was archived when

Instructor: Anne Keuneke

Office: 223 O'Connell

Office Hours: see http://www.ecst.csuchico.edu/~amk/foo/hours.html for current information

Office Phone: (530) 898-5998
Department Phone: (530) 898-6442
email: amk@ecst.csuchico.edu (best way to reach me)
WebPage: http://www.ecst.csuchico.edu/~amk

Textbooks:

• AI for Game Developers (Paperback), by David M. Bourg and Glenn Seemann.· Publisher: O'Reilly Media.· ISBN: 0596005555. Edition: 1 (July 26, 2004).
  
• AI Game Engine Programming (Game Development Series) (Paperback) by Brian Schwab. Publisher: Charles River Media. ISBN: 1584503440. Edition: 1 (September 2004). ( Chapter 1)

Course Description:

This course focuses on the aspects of Artificial Intelligence used in Game Programming. Intelligence is a factor in game design which is being taken more seriously by game developers and producers than ever before. This course will look at AI in games from the early days of Checkers and Chess to making characters appear more interesting and believable as "living" creatures in today's games.
1. The first half of the course will be instruction (Instructor) on various AI techniques that are commonly used in games
   • what are the techniques?
   • what problems are they good for?
   • how hard is it to get information needed? (Knowledge Representation)
   • how hard is it to know when to use it? (Control)
   • how hard is it to implement?
2. The second half of the course will be a survey of games types (Student Research) with consideration of what AI techniques were used (or not)

Language Choice: Whatever programming language you choose - as long as you can provide a demo (if you chose to do a program)

Attendance: Required. The course is a seminar. The first half of the course will be discussion on AI techniques and their positive and negative attributes. Class discussion will be interactive. The more you understand the various AI techniques, the easier it will be to identify their use (or potential use) in games.

Goals: To gain a coverage of the techniques used in Artificial Intelligence to support reasoning skills. This course is not just for "gamers". The AI techniques used in game programming are also used in other applications which expect intelligent behavior by the computer. Specifically, the goal is not to create a specific game, but rather to learn the AI techniques so that a game designer can know what techniques are available and possible.

Course Requirements: One project with an (oral (if local) and written) presentation:
Given the variety of student capabilities in game programming, students will have an option

1. An in-depth research paper on a particular AI game genre --or advanced technique(s)-- complete with references and bibliography. Results should be much like the chapters in Part II of the Schwab book (at least in form - content of your papers should show more depth into particulars given your research)
   OR
   
2. An implemented project with running code illustrating a technique. Code can be either completely new, or useful intelligent adaptations from cited resources. Write-up must be provided to illustrate technique used, goodness of code, etc.

Whatever is chosen, your submission cannot be something that is copied right out of the text(s). Specifically, research (and code snippets) must be done with references and a bibliography provided. Access to potential paper references (with abstracts of content) can be seen on many AI Game websites. Caution: Be leery of the websites that are selling a given game. Be critical of their claims and see if you can justify them (or refute them).
An email to read well before your final draft of your research paper.
Plagiarism will not be tolerated so make sure to properly reference all materials used.

Grade Evaluation Procedures:

• Attendance/Participation 10% (obviously only when offered on campus)
• Project presentation        20%
• Project thoroughness      20%
• Project document/code   50%

Topics (subject to adaptation):

AI Topics include:

• machine learning (e.g., neural nets, genetic algorithms)
• knowledge acquisition (e.g., for decision trees),
• decision-making processes (e.g., rule-based systems, probabilities),
• pattern recognition,
• use of intelligent agents,
• dealing with adversaries