ABSTRACT
Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines. EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines. This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines. Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an appropriate behavior characterization is used to guide the search for diverse solutions.
- Maren Awiszus, Frederik Schubert, and Bodo Rosenhahn. 2021. World-GAN: a Generative Model for Minecraft Worlds. In IEEE Conference on Games.Google ScholarDigital Library
- Matthew Barthet, Antonios Liapis, and Georgios N. Yannakakis. 2022. Open-Ended Evolution for Minecraft Building Generation. IEEE Transactions on Games (2022).Google Scholar
- Hans-Georg Beyer and Hans-Paul Schwefel. 2002. Evolution Strategies - A Comprehensive Introduction. Natural Computing 1, 1 (2002), 3--52.Google ScholarDigital Library
- Antoine Cully. 2021. Multi-Emitter MAP-Elites: Improving Quality, Diversity and Data Efficiency with Heterogeneous Sets of Emitters. In Genetic and Evolutionary Computation Conference. ACM.Google Scholar
- Antoine Cully, Jeff Clune, Danesh Tarapore, and Jean-Baptiste Mouret. 2015. Robots that can adapt like animals. Nature 521, 7553 (2015), 503--507. Google ScholarCross Ref
- Kalyanmoy Deb and Ram Bhushan Agrawal. 1995. Simulated Binary Crossover For Continuous Search Space. Complex Systems 9, 2 (1995), 115--148.Google Scholar
- Matthew C. Fontaine, Ruilin Liu, Ahmed Khalifa, Jignesh Modi, Julian Togelius, Amy K. Hoover, and Stefanos Nikolaidis. 2021. Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network. In AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- Matthew C. Fontaine, Julian Togelius, Stefanos Nikolaidis, and Amy K. Hoover. 2020. Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space. In Genetic and Evolutionary Computation Conference (Cancún, Mexico). ACM, 94--102.Google Scholar
- Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, and Georgios N. Yannakakis. 2019. Procedural Content Generation through Quality Diversity. In IEEE Conference on Games.Google Scholar
- Djordje Grbic, Rasmus Berg Palm, Elias Najarro, Claire Glanois, and Sebastian Risi. 2021. EvoCraft: A new challenge for open-endedness. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar). Springer, 325--340.Google ScholarDigital Library
- Erin Jonathan Hastings, Ratan K Guha, and Kenneth O Stanley. 2009. Automatic Content Generation in the Galactic Arms Race Video Game. IEEE Transactions on Computational Intelligence and AI in Games 1, 4 (2009), 245--263.Google ScholarCross Ref
- Joel Lehman and Kenneth O. Stanley. 2011. Abandoning Objectives: Evolution Through the Search for Novelty Alone. Evolutionary Computation 19, 2 (2011).Google Scholar
- Jean-Baptiste Mouret and Jeff Clune. 2015. Illuminating Search Spaces by Mapping Elites. arXiv:1504.04909 (2015).Google Scholar
- Sebastian Risi, Joel Lehman, David B. D'Ambrosio, Ryan Hall, and Kenneth O. Stanley. 2016. Petalz: Search-Based Procedural Content Generation for the Casual Gamer. IEEE Transactions on Computational Intelligence and AI in Games 8, 3 (2016), 244--255.Google ScholarCross Ref
- Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever. 2017. Evolution Strategies as a Scalable Alternative to Reinforcement Learning. arXiv:1703.03864 http://arxiv.org/abs/1703.03864Google Scholar
- Jacob Schrum, Vanessa Volz, and Sebastian Risi. 2020. CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation. In Genetic and Evolutionary Computation Conference. ACM.Google ScholarDigital Library
- Noor Shaker, Miguel Nicolau, Georgios N Yannakakis, Julian Togelius, and Michael O'neill. 2012. Evolving Levels for Super Mario Bros Using Grammatical Evolution. In IEEE Computational Intelligence and Games. 304--311.Google Scholar
- Noor Shaker, Julian Togelius, and Mark J Nelson. 2016. Procedural Content Generation in Games. Springer.Google Scholar
- Kenneth O. Stanley. 2007. Compositional Pattern Producing Networks: A Novel Abstraction of Development. Genetic Programming and Evolvable Machines 8, 2 (2007), 131--162.Google ScholarDigital Library
- Shyam Sudhakaran, Djordje Grbic, Siyan Li, Adam Katona, Elias Najarro, Claire Glanois, and Sebastian Risi. 2021. Growing 3D Artefacts and Functional Machines with Neural Cellular Automata. In Conference on Artificial Life.Google ScholarCross Ref
- Adam Summerville and Michael Mateas. 2016. Super Mario as a String: Platformer Level Generation via LSTMs. In 1st International Joint Conference of DiGRA and FDG.Google Scholar
- Bryon Tjanaka, Matthew C. Fontaine, Julian Togelius, and Stefanos Nikolaidis. 2022. Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning. In Genetic and Evolutionary Computation Conference (Boston, Massachusetts). ACM.Google Scholar
- Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam M. Smith, and Sebastian Risi. 2018. Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network. In Genetic and Evolutionary Computation Conference (Kyoto, Japan). ACM.Google ScholarDigital Library
Index Terms
- Evolving Flying Machines in Minecraft Using Quality Diversity
Recommendations
A comparison of illumination algorithms in unbounded spaces
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference CompanionIllumination algorithms are a new class of evolutionary algorithms capable of producing large archives of diverse and high-performing solutions. Examples of such algorithms include Novelty Search with Local Competition (NSLC), the Multi-dimensional ...
Comparing multimodal optimization and illumination
GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference CompanionIllumination algorithms are a recent addition to the evolutionary computation toolbox that allows the generation of many diverse and high-performing solutions in a single run. Nevertheless, traditional multimodal optimization algorithms also search for ...
An Extended Study of Quality Diversity Algorithms
GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference CompanionIn a departure from conventional optimization where the goal is to find the best possible solution, a new class of evolutionary algorithms instead search for quality diversity (QD) -- a maximally diverse collection of individuals in which each member is ...
Comments