Biomaker CA: a Biome Maker project using Cellular Automata

• URL: http://arxiv.org/abs/2307.09320v1
• Date: Tue, 18 Jul 2023 15:03:40 GMT
• Title: Biomaker CA: a Biome Maker project using Cellular Automata
• Authors: Ettore Randazzo and Alexander Mordvintsev
• Abstract summary: We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA) In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment. We show how this project allows for several different kinds of environments and laws of 'physics', alongside different model architectures and mutation strategies.
• Score: 69.82087064086666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually reproduce with variation so that a biome survives for long timelines. We simulate complex biomes by means of CA rules in 2D grids and parallelize all of its computation on GPUs through the Python JAX framework. We show how this project allows for several different kinds of environments and laws of 'physics', alongside different model architectures and mutation strategies. We further analyze some configurations to show how plant agents can grow, survive, reproduce, and evolve, forming stable and unstable biomes. We then demonstrate how one can meta-evolve models to survive in a harsh environment either through end-to-end meta-evolution or by a more surgical and efficient approach, called Petri dish meta-evolution. Finally, we show how to perform interactive evolution, where the user decides how to evolve a plant model interactively and then deploys it in a larger environment. We open source Biomaker CA at: https://tinyurl.com/2x8yu34s .

Related papers

• Genesis: Towards the Automation of Systems Biology Research [32.67169575874115]
  We are developing a next generation robot scientist called Genesis. We aim to demonstrate that an area of science can be investigated using robot scientists unambiguously faster, and at lower cost, than with human scientists. Here we report progress on the Genesis project.
  arXiv  Detail & Related papers  (2024-08-20T09:40:43Z)
• Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata Ecosystems [0.0]
  This paper presents Coralai, a framework for exploring diverse ecosystems of Neural Cellular Automata (NCA) We provide an exploratory experiment implementing physics inspired by slime mold behavior showcasing the emergence of competition between sessile and mobile organisms. We conclude by outlining future work to discover simulation parameters through measures of multi-scale complexity and diversity.
  arXiv  Detail & Related papers  (2024-06-14T01:24:01Z)
• Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems [59.117526206317116]
  We show that CELL can adaptively evolve into different models for different tasks and data. Experiments on four real-world datasets demonstrate that CELL significantly outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-05-29T02:35:23Z)
• BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning [77.90250740041411]
  This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data.
  arXiv  Detail & Related papers  (2024-02-27T12:43:09Z)
• BioCLIP: A Vision Foundation Model for the Tree of Life [34.187429586642146]
  We release TreeOfLife-10M, the largest and most diverse ML-ready dataset of biology images. We then develop BioCLIP, a foundation model for the tree of life. We rigorously benchmark our approach on diverse fine-grained biology classification tasks.
  arXiv  Detail & Related papers  (2023-11-30T18:49:43Z)
• Bio-inspired spike-based Hippocampus and Posterior Parietal Cortex models for robot navigation and environment pseudo-mapping [52.77024349608834]
  This work proposes a spike-based robotic navigation and environment pseudomapping system. The hippocampus is in charge of maintaining a representation of an environment state map, and the PPC is in charge of local decision-making. This is the first implementation of an environment pseudo-mapping system with dynamic learning based on a bio-inspired hippocampal memory.
  arXiv  Detail & Related papers  (2023-05-22T10:20:34Z)
• Towards Large-Scale Simulations of Open-Ended Evolution in Continuous Cellular Automata [0.0]
  We build large-scale evolutionary simulations using parallel computing framework JAX. We report a number of system design choices, including implicit implementation of genetic operators. We propose several factors that may further facilitate open-ended evolution.
  arXiv  Detail & Related papers  (2023-04-12T06:40:11Z)
• BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining [140.61707108174247]
  We propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA.
  arXiv  Detail & Related papers  (2022-10-19T07:17:39Z)
• Using Genetic Algorithms to Simulate Evolution [0.0]
  We are able to predict future changes as well as simulate the process using genetic algorithms. By optimizing genetic algorithms to hold entities in an environment, we are able to assign varying characteristics such as speed, size, and cloning probability. Learning about how species grow and evolve allows us to find ways to improve technology, help animals going extinct to survive, and figure* out how diseases spread.
  arXiv  Detail & Related papers  (2022-09-14T00:23:06Z)
• The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents [51.94554095091305]
  We argue for an introspective agent, which considers its own abilities in the context of its environment. Just as in nature, we hope to reframe strategy as one tool, among many, to succeed in an environment.
  arXiv  Detail & Related papers  (2022-01-02T20:14:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.