Spring 2008 AI for Game Programming

Spring 2008 CAP 4932 AI for Game Programming

MW 3:00PM - 4:15PM in HEC room 110

Instructor: Dr. Kenneth Stanley

Email: kstanley@cs.ucf.edu
Website: http://www.cs.ucf.edu/~kstanley
EPlex Research Group: http://eplex.cs.ucf.edu
Office: Harris 332

Office Hours (starting 1/8/08): Mondays 4:30-6pm and Tuesdays 3-4:30pm

TA: Adam Campbell
TA Office Hours: Tuesday and Thursday 4:30-6pm in HEC 303

Link to Homework Assignments and Lectures

Texts

Programming Game AI by Example by Mat Buckland, Wordware Publishing, Inc. (2004)
AI Techniques for Game Programming by Mat Buckland, Course Technology PTR (2002)
Software and Source Code
Demo code for Programming Game AI by Example is available at http://www.wordware.com/files/ai. The "Buckland" files contain the code.
Any version of NEAT can be used for the class homework assignmentsts. A number of versions are available here: http://www.cs.ucf.edu/~kstanley/#software
The latest clean version written by myself is rtNEAT, available here. A stripped down version of my non-real-time code is neatVS.zip, refitted for easy compiling by Jared Johnson..
Overview
The video game industry is a major consumer of artificial intelligence (AI) technology. It is one of the few areas of computer science wherein regular consumers routinely interact with cutting edge techniques. Video game consumers are demanding AI innovation to make games more interesting and fun. The course introduces a broad range of AI techniques for games, contrasting recent cutting-edge approaches with more traditional ones. Both video games and turn-based games (i.e. board games) are covered from a variety of perspectives. Topics range from standard techniques such as scripting and path-finding to recent innovations like reinforcement learning and real-time neuroevolution. Industry needs and priorities are also addressed. The course focuses on practical application and hands-on experience, culminating in a final project. Thus, students will leave equipped to apply the latest approaches to real games.
Grading Policy
Three project-based homework assignments (60% of grade)

Foundations: Create a simple experimental platform with agents and sensors (with partner)
Algorithms: Implement pathfinding and evolution in platform (individual)
Final Project: Convert it into a simple game (with partner from first assignment)

Two exams testing comprehensive knowledge (40% of grade)

Midterm: Foundations and early algorithms
Final: Comprehensive

Late assignments lose 10% the first day late, 40% the second, and are not accepted after that.
Cheating
All of the work that you turn in or present must be your own. Cheating, plagiarism, and any other form of academic dishonesty will be penalized. The minimum penalty for cheating will include:

An automatic zero on the assignment -- this grade may not be dropped; and
reduction of your final grade by one letter grade; and
notification of the incident to the UCF Office of Student Conduct.

Plagiarism and paraphrasing are forms of cheating. Plagiarism is the presentation of others' ideas and writings as your own. Paraphrasing is taking someone else's sentence, changing a few words, and then presenting it as your own. Both are unacceptable in this class.
Important Students may not turn in code from either of Mat Buckland's book with the exception of Mat Buckland's NEAT, engine code (i.e. code not specifying AI algorithms), or otherwise specified by Dr. Stanley. Turning in book code will be penalized as plagiarism.

Schedule
January 7 Class introduction, AI & the Video Game Industry
January 9 Game Engines for AI
January 14 Mathematical Foundations
January 16 The Perception and Action Loop
January 21 No class: Martin Luther King Jr. Day
January 23 Sensor Computations
January 28 Internal State, Finite State Machines, and Scripting
January 30 Pathfinding and Search
February 4 Board Games and Game Tree Search
February 6 Neural Networks and Backpropagation
February 11 Evolutionary Computation
February 13 Neuroevolution
February 18 Midterm
February 20 NeuroEvolution of Augmenting Topologies (NEAT)
February 25 Real-time Neuroevolution and NeuroEvolving Robotic Operatives (NERO)
February 27 More NERO
March 3 Coevolution and multiagent learning
March 5 Traditional vs. Cutting-edge Contrasts and Hybrid Techniques
March 10 No class: Spring break
March 12 No class: Spring break
March 17 Realtime vs. Turn-based and Fun vs, Optimal
March 19 Coordinate Systems, Perspectives, and Control; Mitigating Computational Complexity
March 24 AI in Multiplayer Games and Massive Multiplayer Online Worlds
March 26 Interactive Evolution
March 31 AI for Content Generation
April 2 Game Industry Perspectives
April 7 TBD
April 14 Presentations
April 16 Presentations
April 21 Final held in class
Final: April 23rd
1pm-3:50pm Complete Finals Schedule