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Creators/Authors contains: "Liapis, Antonios"

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  1. ; ; ; ; (, 2019 IEEE Conference on Games (CoG))
    Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work. 
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    Full Text Available 
  2. ; ; ; ; ; (, FDG Workshop on Procedural Content Generation)
    This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2. Gener- ation is split into two steps, initially producing the architectural layout of the level as its walls and floor tiles, and then furnishing it with game objects representing the player’s start and goal position, challenges and rewards. Three layout creators and three furnishers are introduced in this paper, which can be combined in different ways in the two-step generative process for producing diverse dun- geons levels. Layout creators generate the floors and walls of a level, while furnishers populate it with monsters, traps, and treasures. We test the generated levels on several expressivity measures, and in simulations with procedural persona agents. 
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    Accepted Manuscript Full Text Available