A spatially-structured PCG method for content diversity in a Physics-based simulation game

This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n- body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network

Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels. While GAN generated levels are stylistically similar to human-authored examples, human designers often want to explore the generative design space of GANs to extract interesting levels. However, human designers find latent vectors opaque and would rather explore along dimensions the designer specifies, such as number of enemies or obstacles. We propose using state-of-the-art quality diversity algorithms designed to optimize continuous spaces, i.e. MAP-Elites with a directional variation operator and Covariance Matrix Adaptation MAP-Elites, to efficiently explore the latent space of a GAN to extract levels that vary across a set of specified gameplay measures. In the benchmark domain of Super Mario Bros, we demonstrate how designers may specify gameplay measures to our system and extract high-quality (playable) levels with a diverse range of level mechanics, while still maintaining stylistic similarity to human authored examples. An online user study shows how the different mechanics of the automatically generated levels affect subjective ratings of their perceived difficulty and appearance.Comment: Accepted to AAAI 202

3D Self-Rescue Game for Tunnel Fire

This thesis aims to create a 3D game in Unity to practice self-rescue training during emergencies involving fire, using tunnels that are modeled procedurally. Closing down tunnels to practice tunnel safety is cost-inefficient and an impediment to the tunnels' operation and infrastructure. This motivation and previous work on tunnel safety inspired and laid the basis for this thesis. The research concluded that it is possible to create a self-rescue game in Unity, using tunnel models created procedurally. The game created for Unity can populate a tunnel model with objects and create a playable self-rescue scenario and opens for the possibility to use any single tube tunnel model. The hope is to inspire researchers to continue this research to create more accurate models and more realistic self-rescue scenarios for the public.This thesis aims to create a 3D game in Unity to practice self-rescue training during emergencies involving fire, using tunnels that are modeled procedurally. Closing down tunnels to practice tunnel safety is cost-inefficient and an impediment to the tunnels' operation and infrastructure. This motivation and previous work on tunnel safety inspired and laid the basis for this thesis. The research concluded that it is possible to create a self-rescue game in Unity, using tunnel models created procedurally. The game created for Unity can populate a tunnel model with objects and create a playable self-rescue scenario and opens for the possibility to use any single tube tunnel model. The hope is to inspire researchers to continue this research to create more accurate models and more realistic self-rescue scenarios for the public

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose AMIGo, a novel agent incorporating -- as form of meta-learning -- a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals to train a goal-conditioned "student" policy in the absence of (or alongside) environment reward. Specifically, through a simple but effective "constructively adversarial" objective, the teacher learns to propose increasingly challenging -- yet achievable -- goals that allow the student to learn general skills for acting in a new environment, independent of the task to be solved. We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks where other forms of intrinsic motivation and state-of-the-art RL methods fail.Comment: 18 pages, 6 figures, published at The Ninth International Conference on Learning Representations (2021

Generating video game puzzles through planning

Planning is an AI concept that is well-known in the video game industry and has been applied to video games. A large number of these applications have focused on using planning to control the behaviours of agents within video games. However, there is comparatively little research about the application of planning in video games for non-behavioural AI; that is the focus of this work. Many video games consist of puzzles or puzzle elements that players have to solve. Puzzles have a defined search space that can often be considered a planning domain, and planners provide a useful tool for finding a valid series of actions that can efficiently solve such problems. The aim of this work is to explore whether it is possible to create valid and challenge-appropriate puzzles for players in video games through automated planning

Dynamically adjusting game-play in 2D platformers using procedural level generation

The rapid growth of the entertainment industry has presented the requirement for more efficient development of computerized games. Importantly, the diversity of audiences that participate in playing games has called for the development of new technologies that allow games to address users with differing levels of skills and preferences. This research presents a systematic study that explored the concept of dynamic difficulty using procedural level generation with interactive evolutionary computation. Additionally, the design, development and trial of computerized agents the play game levels in the place of a human player is detailed. The work presented in this thesis provides a solution to the rapid growth of the entertainment industry whilst providing a more effective means for developing computerized games