Creating appropriate challenge level game opponent by the use of dynamic difficulty adjustment

The goal for video game AI (artificial intelligence) is to generate AI that is at appropriate challenge level. Most existing game AI is implemented by FSM (Finite State Machine) which has drawbacks in the three respects: requirement of designer´s intensive participation; can´t adjust strategies or difficulty dynamically; no planning and looking forward. Contribution of this paper is to propose DDA (dynamic difficulty adjustment) as an approach to create appropriate challenge level game opponent. During the research, the prey and predator genre game of Dead-End is used as test-bed to prove the proposed theory. Based on the Dead-End test-bed, I proposed two kinds of DDA which are DDA by “time-constrained-CI” and DDA by “knowledge-based-time-constrained-CI”. As the latter is based on knowledge, it is more computational resource efficient than the former and thus more applicable for multi-player online games, while the former is only applicable for the standalone PC game.