Mindset: The 2.5D Platformer

Mindset is a 2.5D platformer video game developed in Unreal Engine. The player must navigate different levels and overcome various challenges on a quest to reach the end of the game. Each level of Mindset is made to represent a different emotion in the protagonist’s life such as contentment, anger, and sadness. Part of the core functionality of the game is this idea that there are two dimensions to every level, a foreground and a background. The challenges in each level incorporate the core mechanic of the game known as “plane shifting” in which the player swaps from foreground to background or vice versa. The challenges in each level revolve around this idea of plane shifting, and it is up to the player to figure out how to solve them

Automating Game Design In Three Dimensions

We describe ANGELINA-5, a new iteration of the AN- GELINA framework for investigating and building software which automates the process of videogame design. ANGELINA-5 is the first automated game design tool that produces 3D games. We outline here the system’s structure, the challenges inherent in building an auto- mated game designer in a modern game engine, and we discuss the future research directions for the project

Virtual player design using self-learning via competitive coevolutionary algorithms

The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.This work is partially supported by Spanish MICINN under Project ANYSELF (TIN2011-28627-C04-01),3 by Junta de Andalucía under Project P10-TIC-6083 (DNEMESIS) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

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