Dynamic Player-Based Level Adjustment
Dynamic Player-Based Level Adjustment is my Masters level thesis both criticizing the modern use of Dynamic Difficulty Adjustment while also proposing an alternative which is player centric. This paper explores why Dynamic Difficulty Adjustment is in a strange place in modern gaming, specifically ARPG games for scope. It goes on to explain a play style focused adjustment system to engage players based on how they want to play, not just how good they are at a certain task in the game.
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Utilizing testing, a full custom prototype application, tool sets built in unreal to establish how a player is playing, and finally a custom built semi-AI to adjust the levels, DPLA is valid at being able to adjust based on player archetypes established. I go on to extrapolate the results into many different game genres, such as FPS, FPS matchmaking, third person puzzle games, and even physical games such as Billiards.
ABSTRACT
DDA features missing parts as it has, over time, focused exclusively on statistical changes while ignoring holistic changes based on the players play style. Dynamic Player-based Level Adjustment, DPLA, as a system, provides an alternative, and additive, method for changing the game based on different points of player identification. By adjusting a level in this way, it aims to solve several issues inherent with the statistical changes of DDA. This thesis explores the workflow and implementation of DPLA system into an ARPG test environment and evaluates the success, failures, and future of DPLA as a game level development alternative to DDA or used as an additive component to alleviate the issues that DDA creates in regards to a player’s unique play style. Ultimately, testing with a built digital ARPG prototype showed that DPLA and the Points of Player Identification system was able to adjust the maps and accurately showcase the player’s intended playstyle. These conclusions resulted in DPLA being a reasonable addition, or alternative, to traditional game development DDA.
