With the algorithm type selected, difficulty scaling techniques now need to be selected. From the scaling techniques listed below, the current team preferred is Scaling Enemy Attributes for a simpler implementation or DDA for a more complex implementation, to still be decided.

Dynamic Difficulty Adjustment, DDA

Works by adapting based on player performance, providing a constant feeling of challenge.

Pros

• Keeps gameplay engaging by adjusting the challenge level dynamically.

Cons

• Can feel unpredictable if the numbers do not properly represent the nature of the game.

Examples

Resident Evil 4 utilises DDA to adjust enemy aggression and skill.

Procedural Content Generation

Mostly unrelated but utilises an AI to generate levels, enemies, and challenges dynamically.

Pros

• More varied experiences, reduces repetition

Cons

• May lead to inconsistent difficulty

Examples

Minecraft utilises procedural generation to generate its worlds.

Adaptive AI

Similar to DDA but uses machine learning to modify the behaviour based on the players strategies and not just performance indictors.

Pros

• Evolves based on player actions, making encounters more engaging compared to other methods

Cons

• Can lead to frustration if the adaption is too agressive.

Examples

Left 4 dead uses an "AI Director" to modify enemy spawns and difficulty.

Scaling Enemy Attributes

Enemy characteristics are modified based on the players performance attributes, i.e. size of the player circle leads to increase of enemy bot size being spawned in.

Pros

• Simple to implement
• Ensures a steady challenge

Cons

• Can feel artificial, where enemies become "bullet sponges"

Examples

Dark souls increases enemy stats in New Game Plus Mode.

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P. Spronck, "Adaptive Game AI," in Proceedings of the 5th International Conference on Intelligent Games and Simulation (GAME-ON 2004), 2004, pp. 1-8. [Online]. Available: https://www.spronck.net/pubs/SpronckGAMEON2004.pdf. [Accessed: Apr. 18, 2025].

J. Doe, "AI in Game Design: Challenges and Solutions," Multimedia Tools and Applications, vol. 83, no. 2, pp. 1234-1256, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s11042-024-18768-x. [Accessed: Apr. 18, 2025].