The experience-driven perspective

Abstract

Ultimately, content is generated for the player. But so far, our algorithms have not taken specific players into account. Creating computational models of a player's behaviour, preferences, or skills is called player modelling. With a model of the player, we can create algorithms that create content specifically tailored to that player. The experience-driven perspective on procedural content generation provides a framework for content generation based on player modelling; one of the most important ways of doing this is to use a player model in the evaluation function for search-based PCG. This chapter discusses different ways of collecting and encoding data about the player, primarily player experience, and ways of modelling this data. It also gives examples of different ways in which such models can be used.

Enemy Within: Long-term Motivation Effects of Deep Player Behavior Models for Dynamic Difficulty Adjustment

“…The underlying proficiency variable λ approximated the player's performance level by being increased whenever he successfully completed Eternal Challenge and decreased at the characters death or temporal expiry of the countdown. This way, HPT produces a typical "rubberbanding" effect between player-specific thresholds of lack of challenge and excessive challenge, which is one of the most common ways to enable flow-states to arise in traditional DDA [47] and was constructed by following the inspiration of these approaches [17,23,30,47] in combination with fine-tuning by the developers operating the server to find a range covering too easy, too hard and enough configurability in between for every observed player. Increased whenever Eternal Challenge λ difficulty level was completed successfully, decreased upon death or timeout.…”

Guest Editorial Special Issue on User Experience of AI in Games

“…A defining feature of this case is that games here are usually seen as an interesting and challenging testbed to develop an AI for, but the games itself are seen as a means to an end. In contrast, the second case is concerned with using AI to improve games for the human players, i.e., develop AIs to elicit specific, usually positive, emotions [6]. Examples here include procedural content generation (PCG) to increase replayability and reduce repetitiveness, or the generation of believable nonplayer character (NPC) behavior and to increase immersion and engagement.…”