Publicações

2022

• 19/02/2022 • Breno M. F. Viana, Leonardo T. Pereira, Claudio F. M. Toledo

  Illuminating the Space of Enemies Through MAP-Elites

  Palavras-chave: enemy generation, procedural content generation, video game, evolutionary algorithm, map-elites

  Abstract • BibTeX • Preprint

  Abstract

  Action-Adventure games have several challenges to overcome, where the most common are enemies. The enemies' goal is to hinder the players' progression by taking life points, and the way they hinder this progress is distinct for different kinds of enemies. In this context, this paper introduces an extended version of an evolutionary approach for procedurally generating enemies that target the enemy's difficulty as the goal. Our approach advances the enemy generation research by incorporating a MAP-Elites population to generate diverse enemies without losing quality. The computational experiment showed the method converged most enemies in the MAP-Elites in less than a second for most cases. Besides, we experimented with players who played an Action-Adventure game prototype with enemies we generated. This experiment showed that the players enjoyed most levels they played, and we successfully created enemies perceived as easy, medium, or hard to face.

  BibTeX • Copiar

  @article{viana2022illuminatingenemies,
    title={Illuminating the Space of Enemies Through MAP-Elites},
    author={Viana, Breno M. F. and Pereira, Leonardo T. and Toledo, Claudio F. M.},
    journal={arXiv preprint arXiv:2202.09615},
    year={2022}
  }
  

• 18/02/2022 • Breno M. F. Viana, Leonardo T. Pereira, Claudio F. M. Toledo

  Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites

  Palavras-chave: evolutionary algorithm, map-elites, procedural content generation, level generation, mission generation, video game

  Abstract • BibTeX • Preprint

  Abstract

  Procedural Content Generation (PCG) methods are valuable tools to speed up the game development process. Moreover, PCG may also present in games as features, such as the procedural dungeon generation (PDG) in Moonlighter (Digital Sun, 2018). This paper introduces an extended version of an evolutionary dungeon generator by incorporating a MAP-Elites population. Our dungeon levels are discretized with rooms that may have locked-door missions and enemies within them. We encoded the dungeons through a tree structure to ensure the feasibility of missions. We performed computational and user feedback experiments to evaluate our PDG approach. They show that our approach accurately converges almost the whole MAP-Elite population for most executions. Finally, players' feedback indicates that they enjoyed the generated levels, and they could not indicate an algorithm as a level generator.

  BibTeX • Copiar

  @article{viana2022illuminatinglevels,
    title={Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites},
    author={Viana, Breno M. F. and Pereira, Leonardo T. and Toledo, Claudio F. M.},
    journal={arXiv preprint arXiv:2202.09301},
    year={2022}
  }
  

• 14/02/2022 • Breno M. F. Viana, Leonardo T. Pereira, Claudio F. M. Toledo, Selan R. dos Santos, Silvia M. D. M. Maia

  Feasible-Infeasible Two-Population Genetic Algorithm to evolve dungeon levels with dependencies in barrier mechanics

  Palavras-chave: level generation, procedural content generation, barrier mechanics, video games, constrained optimization

  Abstract • BibTeX • Full Paper

  Abstract

  This paper presents a search-based solution for the generation of dungeon levels with barrier mechanics and the placement of challenges and rewards in the levels’ rooms. The barrier is a feature that temporarily blocks the player’s progression, where one or more keys will unblock the way. The placement of barriers and keys must satisfy some constraints since the player cannot be stuck during the gameplay. Feasible-Infeasible Two-Population Genetic Algorithm (FI2Pop GA) evolves a grid representation that handles the level dependencies of barrier mechanics. We propose the concept of ordered regions to control the availability of keys better in the levels and procedures to create levels with more diversity in their contents. Data to measure the variety of the generated content is collected based on map linearity, mission linearity, leniency, and path redundancy. We analyzed our results through expressive range analysis, and it shows that our approach can generate a wide variety of playable levels.

  BibTeX • Copiar

  @article{
    VIANA2022108586,
    title = {Feasible–Infeasible Two-Population Genetic Algorithm to evolve dungeon levels with dependencies in barrier mechanics},
    journal = {Applied Soft Computing},
    volume = {119},
    pages = {108586},
    year = {2022},
    issn = {1568-4946},
    doi = {https://doi.org/10.1016/j.asoc.2022.108586},
    url = {https://www.sciencedirect.com/science/article/pii/S1568494622000989},
    author = {Breno M. F. Viana and Leonardo T. Pereira and Claudio F. M. Toledo and Selan R. {dos Santos} and Silvia M. D. M. Maia},
    keywords = {Level generation, Procedural content generation, Barrier mechanics, Video games, Constrained optimization},
    abstract = {This paper presents a search-based solution for the generation of dungeon levels with barrier mechanics and the placement of challenges and rewards in the levels’ rooms. The barrier is a feature that temporarily blocks the player’s progression, where one or more keys will unblock the way. The placement of barriers and keys must satisfy some constraints since the player cannot be stuck during the gameplay. Feasible–Infeasible Two-Population Genetic Algorithm (FI2Pop GA) evolves a grid representation that handles the level dependencies of barrier mechanics. We propose the concept of ordered regions to control the availability of keys better in the levels and procedures to create levels with more diversity in their contents. Data to measure the variety of the generated content is collected based on map linearity, mission linearity, leniency, and path redundancy. We analyzed our results through expressive range analysis, and it shows that our approach can generate a wide variety of playable levels.}
  }
  

2021

• 18/10/2021 • Leonardo T. Pereira, Breno M. F. Viana, Claudio F. M. Toledo

  Procedural Enemy Generation through Parallel Evolutionary Algorithm

  Palavras-chave: enemy generation, procedural content generation, video games, parallel evolutionary algorithm

  Abstract • BibTeX • Full Paper • PDF

  Abstract

  This research presents a Parallel Evolutionary Algorithm (PEA) that generates enemies with diverse characteristics, such as the enemy's health, weapons, and movement. Our PEA aims to create enemies matching their difficulty degrees with the difficulty goal given as input parameter. We designed our algorithm in this way to be future used in an online adaptive generation system. We experimented with a set of generated enemies with an Action-Adventure game prototype as a testbed. The results show that players evaluated our approach positively, successfully creating enemies considered easy, medium, or hard to face, as defined by their original fitness' target value. Besides, the players found the game fun to play for all difficulty levels played, and the perceived challenge rose as the PEA fitness was higher. In terms of performance results, our PEA converged into the input solution in less than a second for most cases, denoting its future use in online adaptive applications.

  BibTeX • Copiar

  @INPROCEEDINGS{9637547,
    author={Pereira, Leonardo T. and Viana, Breno M. F. and Toledo, Claudio F. M.},
    booktitle={2021 20th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames)},
    title={Procedural Enemy Generation through Parallel Evolutionary Algorithm},
    year={2021},
    volume={},
    number={},
    pages={126-135},
    doi={10.1109/SBGames54170.2021.00024}
  }