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.

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This paper presents a new method for the discovery of relevant fuzzy rules using the Pseudo-Bacterial Genetic Algorithm (PBGA). The PBGA was proposed by the authors as a new approach combining a genetic algorithm with a local improvement mechanism inspired by a process in bacterial genetics, named bacterial operation. The presented system aims the improvement of the quality of the generated fuzzy rules, producing blocks of e ective rules and more compact rule bases. This is achieved by encoding the fuzzy rules in the chromosomes in a suitable form in order to make the bacterial operation more e ective and by using a crossover operation that adaptively decides the cutting points according to the distribution of degrees of truth values of the rules. In this paper, rst, results obtained when using the PBGA for a simple fuzzy modeling problem are presented and compared with other methods. Second, the PBGA is used in the design of a fuzzy logic controller for a semi-active suspension system. The results show the benets obtained with this approach in both of the studied cases.

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Unlike documents, annotation for multimedia information needs to be input as text, not in the form of symbols such as underlines and circles. This is problematic with keyboard input for non-alphabetical languages, especially the East Asian languages such as Chinese and Japanese, because it is labor intensive and imposes a high cognitive load. This study provides a quantitative analysis of the effectiveness of making annotations by hand during a note-taking task in Japanese. Although the lessons learned from this study come from Japanese text input, they are also generally applicable to other East Asian Languages which use ideographic characters such as Chinese. In our study, we focused on both the ergonomic and cognitive aspects and found that during annotation and note-taking task input by hand is more effective than input by keyboard. Finally, we anatomized the keyboard input problem and discuss it in this paper.