Analyzing the expressive range of a level generator

Procedural Content Generation in Games

2016

Self Cite

Evaluating your content generator is a very important task, but difficult to do well. Creating a game content generator in general is much easier than creating a good game content generator-but what is a "good" content generator? That depends very much on what you are trying to create and why. This chapter discusses the importance and the challenges of evaluating content generators, and more generally understanding a generator's strengths and weaknesses and suitability for your goals. In particular, we discuss two different approaches to evaluating content generators: visualizing the expressive range of generators, and using questionnaires to understand the impact of your generator on the player. These methods could broadly be called top-down and bottom-up methods for evaluating generators. I created a generator, now what?The entirety of this book thus far has been focused on how to create procedural content generators, using a variety of techniques and for many different purposes. We hope that, by now, you have gained an appreciation for the strengths and weaknesses of different approaches to PCG, and also the surprises that can come from writing a generative system. We imagine that you also have experienced some of the frustration that can come from debugging a generative system: "is the interesting level I created a fluke, a result of a bug, or a genuine result?"Creating a generator is one thing; evaluating it is another. Regardless of the method followed, generators are evaluated on their ability to achieve the desired goals of the designer (or the computational designer). This chapter reviews methods for achieving that. Arguably, the generation of any content is trivial; the generation of valuable content for the task at hand, on the other hand, is a rather challenging procedure. Further, it is more challenging to generate content that is both valuable and novel.

Section: Top-down Evaluation Via Expressivity Measuresmentioning

confidence: 99%

Evaluating content generators

Shaker

Smith

Procedural Content Generation in Games

2016

Self Cite

“…On the other hand, the expressive range [16] of the generator can assess the variety of possible results when optimizing different objectives. The two dimensions explored in this paper are the graph size and branching factor: both of these metrics are not directly targeted by the objectives, as suggested by [16], and are indicative of the actions the hero has to make and the decisions they have to take respectively. Figure 5 shows heatmaps of the branching factor and graph size values of the final populations of all runs, i.e.…”

Section: Expressivity Analysismentioning

confidence: 99%

Evolving missions to create game spaces

Karavolos

Liapis

2016 IEEE Conference on Computational Intelligence and Games (CIG)

2016

Abstract-This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission's starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.

“…Moreover, the ontology can indirectly specify the parameter vector of the generator: for instance a level with the type of "dungeon" can adjust the parameters of the generator to create levels with low linearity [34], due to the relationship between "dungeon" and "maze". A more ambitious target for semantic game generation is to use machine learning to identify patterns (e.g.…”

Section: Generating Games From Semantic Informationmentioning

confidence: 99%

A holistic approach for semantic-based game generation

Sacco

Liapis

2016 IEEE Conference on Computational Intelligence and Games (CIG)

2016

Abstract-The Web contains vast sources of content that could be reused to reduce the development time and effort to create games. However, most Web content is unstructured and lacks meaning for machines to be able to process and infer new knowledge. The Web of Data is a term used to describe a trend for publishing and interlinking previously disconnected datasets on the Web in order to make them more valuable and useful as a whole. In this paper, we describe an innovative approach that exploits Semantic Web technologies to automatically generate games by reusing Web content. Existing work on automatic game content generation through algorithmic means focuses primarily on a set of parameters within constrained game design spaces such as terrains or game levels, but does not harness the potential of already existing content on the Web for game generation. We instead propose a holistic and more generally-applicable game generation solution that would identify suitable Web information sources and enrich game content with semantic meta-structures.