to create neuro-controlled game opponent from uct-created data
Adaptive Game AI improves adaptability of opponent AI as well as the challenge level of the gameplay, as a result the entertainment of game is augmented. Opponent game AI is usually implemented by scripted rules in video games, but the most updated algorithm of UCT (Upper Confidence bound for Trees) which perform well in computer go can also be used to achieve excellent result to control non-player characters (NPCs) in video games. However, due to computational intensiveness of UCT, it is actually not suitable for Online Games. As it is already known that UCT can create near optimal control, so it is possible to create Neuro-Controlled Game Opponent by off-line learning from the UCT created sample data; finally Neuro-Controlled Game Opponent for Online Games from UCT-Created Data without worry about computational intensiveness is generated. And also if the optimization approach of Neuro-Evolution is applied to the above generated Neuro-Controller, the performance of the opponent AI is enhanced even further.
neuro-controller uct adaptive game ai neuro-evolution dead end