Using Procedural Content Generation (PCG) to implement DDA

Adaptivity in games is generally refers to changing the game content to meet the users needs and preferences. In previous blog posts i have expressed the reasoning behind adapting learning and cognitive training games. Moreover, i have also described the DDA framework for adapting games. In this post i will explain a method called Procedural Content Generation (PCG). I will then discuss how this method can be used to implement the DDA Framework of adaptivity. Okay, so let’s begin.

Procedural content generation (PCG) is an approach to create content automatically with the use of algorithms instead of manual effort (Yannakakis & Togelius, 2011; Togelius et al., 2010). The developments in this field are driven by three main reasons (Togelius et al., 2010). First, PCG shortens the gap between the overwhelming demand for new game content but the lack of finances to generate content manually, which improves the replayability of games as it constantly generates new content. Second, using PCG improves the performance of games in terms of memory consumption as content is ‘unpacked’ only when it is required. Third, exploring PCG might lead to innovations in game design and hence promote creation of completely new genres of games.

What PCG essentially does, is that it creates new game content like maps, enemies, weapons, etc. automatically using computer algorithms. These algorithms are constrained by the parameters defined by the game designer to generate quasi-random yet appropriate and playable game content. As a result, the job of a game designer shifts from designing game content to designing algorithmic parameters. Parameter design defines the structure of a game and clearly states how the algorithm will create the content for the user.

On the other hand, PCG can also be used to cognitive and affective engagement of players by creating content based on the players needs and preferences.This approach is described as EDPCG: Experience-Driven Procedural Content Generation (Yannakakis & Togelius, 2011) and can be utilized by serious game designers to create highly adaptive games which respond to player needs at a fine level.

Image Source: Artificial Intelligence by geralt

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