Evolving interesting maps for a first person shooter

We address the problem of automatically designing maps for first-person shooter (FPS) games. An efficient solution to this procedural content generation (PCG) problem could allow the design of FPS games of lower development cost with near-infinite replay value and capability to adapt to the skills and preferences of individual players. We propose a search-based solution, where maps are evolved to optimize a fitness function that is based on the players’ average fighting time. For that purpose, four different map representations are tested and compared. Results obtained showcase the clear advantage of some representations in generating interesting FPS maps and demonstrate the promise of the approach followed for automatic level design in that game genre.peer-reviewe

Evoluindo representações de mapas para o jogo Cube 2 : Sauerbraten

Monografia (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Este trabalho apresenta alternativas às representações de mapas introduzidos no artigo “Evolving Interesting Maps for a First Person Shooter”, de Luigi Cardamone et al.. Representação de mapas é utilizado para facilitar ou diminuir os custos de desenvolvimento para Jogos de Tiro em Primeira Pessoa. Para esta monografia, desenvolvemos quatro representações de mapas que aderem as técnicas de projeto de níveis do jogo Cube 2: Sauerbraten (C2) descritas por Marc Saltzman em Secrets of the sages: Level design. Visando possibilitar a comparação das representações, implementamos três das quatro representações de mapas descritas em “Evolving Interesting Maps for a First Person Shooter” bem como as representações propostas nesta monografia. Dessa forma, tornouse possível realizar experimentos de comparação baseadas nas mesmas métricas e testes usados por Luigi Cardamone et al.. Os resultados das comparações demonstram que as representações de mapas desenvolvidas para esta monografia podem ser utilizadas como alternativas as descritas em “Evolving Interesting Maps for a First Person Shooter”.This work attempts to create alternative map representations to the ones presented in the research “Evolving Interesting Maps for a First Person Shooter” [5] utilized to facilitate or lessen the development costs of First Person Shooter (FPS)es. We propose four representations that follow the design techniques for the game Cube 2: Sauerbraten (C2) as described by [36], these will be compared to three of the four map representations described in [5] with the same metrics and tests. These comparisons gave us results that show that our map representations can be used as viable alternatives to the ones described in

Multi-level evolution of shooter levels

This paper introduces a search-based generative process for first person shooter levels. Genetic algorithms evolve the level’s architecture and the placement of powerups and player spawnpoints, generating levels with one floor or two floors. The evaluation of generated levels combines metrics collected from simulations of artificial agents competing in the level and theory-based heuristics targeting general level design patterns. Both simulation-based and theory-driven evaluations target player balance and exploration, while resulting levels emergently exhibit several popular design patters of shooter levels.The research was supported, in part, by the FP7 ICT projects C2Learn (project no: 318480) and ILearnRW (project no: 318803), and by the FP7 Marie Curie CIG project AutoGameDesign (project no: 630665).peer-reviewe

Pairing character classes in a deathmatch shooter game via a deep-learning surrogate model

This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets. Focusing on the shooter game genre, the paper explores how deep learning can help build a model which combines the game level structure and the game's character class parameters as input and the gameplay outcomes as output. The model is trained on a large corpus of game data from simulations with artificial agents in random sets of levels and class parameters. The model is then used to generate classes for specific levels and for a desired game outcome, such as balanced matches of short duration. Findings in this paper show that the system can be expressive and can generate classes for both computer generated and human authored levels.peer-reviewe