Spring 2009 CAP 4053 AI for Game Programming
MW 1:30PM - 2:45PM in ENG2 room 105
Instructor: Dr. Kenneth Stanley
EPlex Research Group: http://eplex.cs.ucf.edu
Office: Harris 332
Office Hours (starting 1/12/09): Mondays 3-5pm and Tuesdays 3-4pm
TAs: Sebastian Risi (primary) and Adam Campbell
TA Office Hours: TBD
Link to Homework Assignments and Lectures
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 CodeDemo 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..
OverviewThe 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 PolicyThree 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.
CheatingAll 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.
ScheduleJanuary 7 Class introduction, AI & the Video Game Industry
January 12 Game Engines for AI
January 14 Mathematical Foundations
January 19 No class: Martin Luther King Jr. Day
January 21 The Perception and Action Loop
January 26 Sensor Computations
January 28 Internal State, Finite State Machines, and Scripting
January 30 Pathfinding and Search
February 2 Board Games and Game Tree Search
February 4 Neural Networks and Backpropagation
February 9 Evolutionary Computation
February 11 Evolutionary Computation 2
February 16 Neuroevolution
February 18 Midterm
February 23 NeuroEvolution of Augmenting Topologies (NEAT)
February 25 Real-time Neuroevolution and NeuroEvolving Robotic Operatives (NERO)
March 2 More NERO
March 4 Coevolution and multiagent learning
March 9 No class: Spring break
March 11 No class: Spring break
March 16 Coevolution and multiagent learning 2 and Traditional vs. Cutting-edge Contrasts and Hybrid Techniques
March 18 Project Discussion and Game Design Issues
March 23 Coordinate Systems, Perspectives, and Control; Mitigating Computational Complexity
March 25 Game proposals 1
March 30 Game proposals 2
April 1 Interactive Evolution and AI for Content Generation, AI in Multiplayer Games and Massive Multiplayer Online Worlds
April 6 Game Industry Perspectives
April 8 Galactic Arms Race
April 10 Artificial Life
April 13 ?
April 15 ?
April 20 Presentations
April 22 Presentations
April 27 Closing Remarks and Discussion
Final: May 4th
1pm-3:50pm Complete Finals Schedule