In the most recent versions of popular fighting games like Soul Calibur, Tekken, and Dead or Alive
you can select the types of weapons a player can use, the stage, and costumes. This type of customization
and personalization provides users with the ability to change the look and feel of players and fights but only
on a very high and imprecise level.

While these versions of recent fighting games give users a great deal of freedom, users want more control of the
actual game play and want to be able to customize the moves and skills of a player instead of using the same pre-packaged
animations over and over. This has inspired the creation of fighting games like Virtual Fighter with different modes where
you can train your own players by sparring to endow them with new moves, and by judging their abilities in replay.

Our game makes use of real-time physical simulation with ragdoll physics which is a type of procedural animation that has
been traditionally used to replace static death animations prepackaged in most video games. A ragdoll is a collection of multiple
rigid bodies tied together by a system of constraints that restrict how the bones may move relative to each other. Ragdoll
Masters (link below) uses this sort of simulation as well as Ragdoll Kungfu.

The main algorithm we are using for our AI architecture is based on Q-learning. This is a type of reinforcement learning
technique that works by learning an action-value function that gives the expected utility of taking a given action in a given
state and following a fixed policy thereafter. We expect to do further research in this area of reinforcement learning to further
increase the effectiveness of our AI system.

http://www.soulcalibur.com/
http://www.tekken-official.jp/
http://www.virtua-fighter-4.com/
http://smackdown-vs-raw-game.com/
http://www.ragdollkungfu.com/
http://ragdollsoft.com/ragdollmasters/
http://en.wikipedia.org/wiki/Q-Learning
http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol2/zah/article2.html
http://people.revoledu.com/kardi/tutorial/ReinforcementLearning/index.html