Creating Challengeable and Satisfactory Game Opponent by the Use of CI Approaches

The goal for video game AI (artificial intelligence) is to generate AI that is both challengeable and satisfactory. Most existing game AI is implemented by FSM (finite state machine) which has drawbacks in the three respects: requirement of designer’s intensive participation; no existence of meta-programming; no planning and looking forward. Contributions of the paper is to propose CI (computational intelligence) as an approach to create both challengeable and satisfactory game opponents better than using FSM in the above three respects. During the research, two prey and predator genre games of Dead-End and Pac-Man are used as test-beds to prove the proposed theory. To create a challengeable game opponent, we proposed two CI approaches: “CI-controlled-NPC” and “knowledge-based-CI-controlled-NPC”. As the latter is based on knowledge and is more computational resource efficient than the former, so the latter is more applicable for multi-player online games, while the former is only applicable for standalone PC game. To create a satisfactory game opponent, is to optimize player’s experience through the creation of an even game. To better satisfy the player, we proposed two CI-based DDAs (Dynamic Difficulty Adjustment): DDA by “timeconstrained-CI” and DDA by “knowledge-based-time-constrained-CI”; both of which outperform the existing DDA. As the latter is based on knowledge and more computational resource efficient than the former, so it is more applicable for multi-player online games, while the former is only applicable for standalone PC game.