Abstract. Recently, a small number of papers have appeared in which the authors implement stochastic search algorithms, such as evolutionary computation, to generate game content, such as levels, rules and weapons. We propose a taxonomy of such approaches, centring on what sort of content is generated, how the content is represented, and how the quality of the content is evaluated. The relation between search-based and other types of procedural content generation is described, as are some of the main research challenges in this new field. The paper ends with some successful examples of this approach.

Computational representation of everyday emotional states is a challenging task and, arguably, one of the most fundamental for affective computing. Standard practice in emotion annotation is to ask people to assign a value of intensity or a class value to each emotional behavior they observe. Psychological theories and evidence from multiple disciplines including neuroscience, economics and artificial intelligence, however, suggest that the task of assigning reference-based values to subjective notions is better aligned with the underlying representations.This paper draws together the theoretical reasons to favor ordinal labels for representing and annotating emotion, reviewing the literature across several disciplines. We go on to discuss good and bad practices of treating ordinal and other forms of annotation data and make the case for preference learning methods as the appropriate approach for treating ordinal labels. We finally discuss the advantages of ordinal annotation with respect to both reliability and validity through a number of case studies in affective computing, and address common objections to the use of ordinal data. More broadly, the thesis that emotions are by nature ordinal is supported by both theoretical arguments and evidence, and opens new horizons for the way emotions are viewed, represented and analyzed computationally.

No abstract

We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The gameplay and learning tracks resemble traditional reinforcement learning competitions, the Level generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on humanlike game-playing behavior. We also outline some lessons learned from the competition and its future. The article is written by the four organizers of the competition.

The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this paper identifies game facet orchestration as the central challenge for artificial-intelligence-based game generation, discusses its dimensions, and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, propose how orchestration can be facilitated in a top-down or bottom-up fashion, review indicative preliminary examples of orchestration, and conclude by discussing the open questions and challenges ahead.