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Title
Abstract
Key Takeaways
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Introduction
Subjective Pem
Objective Pem
References
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Psychology
Cognitive Science

Experience-Driven Procedural Content Generation

Profile image of Julian TogeliusJulian Togelius

2011, IEEE Transactions on Affective Computing

https://doi.org/10.1109/T-AFFC.2011.6
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17 pages

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1 file

Abstract

Procedural content generation (PCG) is an increasingly important area of technology within modern human-computer interaction (HCI) design. Personalization of user experience via affective and cognitive modeling, coupled with real-time adjustment of the content according to user needs and preferences are important steps toward effective and meaningful PCG. Games, Web 2.0, interface, and software design are among the most popular applications of automated content generation. The paper provides a taxonomy of PCG algorithms and introduces a framework for PCG driven by computational models of user experience. This approach, which we call Experience-Driven Procedural Content Generation (EDPCG), is generic and applicable to various subareas of HCI. We employ games as an example indicative of rich HCI and complex affect elicitation, and demonstrate the approach's effectiveness via dissimilar successful studies.

Key takeaways
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AI

  1. Experience-Driven Procedural Content Generation (EDPCG) optimizes user experience through affective modeling and real-time content adjustment.
  2. EDPCG applies to diverse HCI fields, enhancing personalization in games, e-commerce, and education.
  3. Player experience modeling combines subjective, objective, and gameplay-based approaches for richer insights.
  4. The cost of game content creation has surged, necessitating automated solutions like EDPCG.
  5. The paper outlines a taxonomy of PCG algorithms and discusses the challenges of evaluating game content quality.
Figures (7)
Fig. 1. The main components of the experience-driven procedural content generator.
Fig. 1. The main components of the experience-driven procedural content generator.
Fig. 2. Example levels generated for two different Super Mario players. The levels generated maximize the modeled fun value for each player. The level on top depicts the level generated for one of the subjects that participated in our experiments while the level below is the level generated for the world champion agent of the Mario Al competition.
Fig. 2. Example levels generated for two different Super Mario players. The levels generated maximize the modeled fun value for each player. The level on top depicts the level generated for one of the subjects that participated in our experiments while the level below is the level generated for the world champion agent of the Mario Al competition.
Fig. 3. The EDPCG framework in detail. The gradient grayscale-colored boxes represent a continuum of pos- sibilities between the two ends of the box while white boxes represent discrete, exclusive, options within the box. The blue arrows illustrate the EDPCG approach fol- lowed for the Super Mario Bros example study described in Section 2.1 [28], [29]: Content quality is assessed via a direct, data-driven evaluation function which is based on a combination of a gameplay-based (model-free), and a subjective (pairwise preference) player experience modeling approach; content is represented indirectly and exhaustive search is applied to generate better content.
Fig. 3. The EDPCG framework in detail. The gradient grayscale-colored boxes represent a continuum of pos- sibilities between the two ends of the box while white boxes represent discrete, exclusive, options within the box. The blue arrows illustrate the EDPCG approach fol- lowed for the Super Mario Bros example study described in Section 2.1 [28], [29]: Content quality is assessed via a direct, data-driven evaluation function which is based on a combination of a gameplay-based (model-free), and a subjective (pairwise preference) player experience modeling approach; content is represented indirectly and exhaustive search is applied to generate better content.
that involves the content being evaluated. Such playthrough might include finding the way out of a maze while not being killed or playing the board game that results from the newly generated rule set against another artificial agent. Features, that map to player experience models, are then extracted from the o bserved gameplay (e.g. did the agent win? How fast? How was the variation in playing styles employed?) and used to ca agent culate the quality value of the content. The artificial might be completely hand-coded, or might be based ona earned behavioral model of human players.
that involves the content being evaluated. Such playthrough might include finding the way out of a maze while not being killed or playing the board game that results from the newly generated rule set against another artificial agent. Features, that map to player experience models, are then extracted from the o bserved gameplay (e.g. did the agent win? How fast? How was the variation in playing styles employed?) and used to ca agent culate the quality value of the content. The artificial might be completely hand-coded, or might be based ona earned behavioral model of human players.
Overview table for the relationship between PEM approaches and types of evaluation functions. PEM includes the Subjective (S), Objective (O) and GamePlay-Based (G) approaches and their hybrids: subjective and objective (SO), objective and gameplay-based (OG), subjective and gameplay-based (SG), and all three combined (SOG). Representative studies surveyed in this article that follow each approach appear at the corresponding table cell. References in parentheses denote that the game content is not considered explicitly as part of the evaluation; the interaction with non-player characters is considered instead. A dash (—) symbolizes an infeasible combination between a PEM approach and an evaluation function type.
Overview table for the relationship between PEM approaches and types of evaluation functions. PEM includes the Subjective (S), Objective (O) and GamePlay-Based (G) approaches and their hybrids: subjective and objective (SO), objective and gameplay-based (OG), subjective and gameplay-based (SG), and all three combined (SOG). Representative studies surveyed in this article that follow each approach appear at the corresponding table cell. References in parentheses denote that the game content is not considered explicitly as part of the evaluation; the interaction with non-player characters is considered instead. A dash (—) symbolizes an infeasible combination between a PEM approach and an evaluation function type.

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