Emotion and Adaptation

I. the origin of species by means of natural selection

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Flow The Psychology Of Optimal Experience

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

Continuing interest in digital games indicated that it would be useful to update Connolly et al.'s (2012) systematic literature review of empirical evidence about the positive impacts and outcomes of games. Since a large number of papers was identified in the period from 2009 to 2014, the current review focused on 143 papers that provided higher quality evidence about the positive outcomes of games. Connolly et al.'s multidimensional analysis of games and their outcomes provided a useful framework for organising the varied research in this area. The most frequently occurring outcome reported for games for learning was knowledge acquisition, while entertainment games addressed a broader range of affective, behaviour change, perceptual and cognitive and physiological outcomes. Games for learning were found across varied topics with STEM subjects and health the most popular. Future research on digital games would benefit from a systematic programme of experimental work, examining in detail which game features are most effective in promoting engagement and supporting learning. The current systematic literature review updates Author (date).The review looks at impacts and outcomes of playing digital games from 2009 to 2014.Multi-component coding of papers, games and learning outcomes was used.Many papers were found with 143 papers providing high quality evidence.Games for entertainment and learning addressed different outcomes.

/pdf/an-update-to-the-systematic-literature-review-of-empirical-oy90nea5x6.pdf

An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games

This book presents the most up-to-date coverage of procedural content generation (PCG) for games, specifically the procedural generation of levels, landscapes, items, rules, quests, or other types of content. Each chapter explains an algorithm type or domain, including fractal methods, grammar-based methods, search-based and evolutionary methods, constraint-based methods, and narrative, terrain, and dungeon generation. The authors are active academic researchers and game developers, and the book is appropriate for undergraduate and graduate students of courses on games and creativity; game developers who want to learn new methods for content generation; and researchers in related areas of artificial intelligence and computational intelligence.

Procedural Content Generation in Games

In this paper, we show that personalized levels can be automatically generated for platform games. We build on previous work, where models were derived that predicted player experience based on features of level design and on playing styles. These models are constructed using preference learning, based on questionnaires administered to players after playing different levels. The contributions of the current paper are (1) more accurate models based on a much larger data set; (2) a mechanism for adapting level design parameters to given players and playing style; (3) evaluation of this adaptation mechanism using both algorithmic and human players. The results indicate that the adaptation mechanism effectively optimizes level design parameters for particular players.

http://julian.togelius.com/Shaker2010Towards.pdf

Towards automatic personalized content generation for platform games

The Level Generation Competition, part of the IEEE Computational Intelligence Society (CIS)-sponsored 2010 Mario AI Championship, was to our knowledge the world's first procedural content generation competition. Competitors participated by submitting level generators - software that generates new levels for a version of Super Mario Bros tailored to individual players' playing style. This paper presents the rules of the competition, the software used, the scoring procedure, the submitted level generators, and the results of the competition. We also discuss what can be learned from this competition, both about organizing procedural content generation competitions and about automatically generating levels for platform games. The paper is coauthored by the organizers of the competition (the first three authors) and the competitors.

/pdf/the-2010-mario-ai-championship-level-generation-track-1xln7q5ib1.pdf

The 2010 Mario AI Championship: Level Generation Track

This paper presents the use of design grammars to evolve playable 2D platform levels through grammatical evolution (GE). Representing levels using design grammars allows simple encoding of important level design constraints, and allows remarkably compact descriptions of large spaces of levels. The expressive range of the GE-based level generator is analyzed and quantitatively compared to other feature-based and the original level generators by means of aesthetic and similarity based measures. The analysis reveals strengths and shortcomings of each generator and provides a general framework for comparing content generated by different generators. The approach presented can be used as an assistive tool by game designers to compare and analyze generators' capabilities within the same game genre.

/pdf/evolving-levels-for-super-mario-bros-using-grammatical-59zek38woj.pdf

Noor Shaker

Papers

Procedural Content Generation in Games

Towards automatic personalized content generation for platform games

The 2010 Mario AI Championship: Level Generation Track

Evolving levels for Super Mario Bros using grammatical evolution

Imitating human playing styles in Super Mario Bros