Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization

• URL: http://arxiv.org/abs/2212.07906v2
• Date: Fri, 24 Mar 2023 10:48:42 GMT
• Title: Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization
• Authors: Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Pierre-Yves Oudeyer, Cl\'ement Moulin-Frier and Bert Wang-Chak Chan
• Abstract summary: Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics. We show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics.
• Score: 16.11867905890754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The design of complex self-organising systems producing life-like phenomena, such as the open-ended evolution of virtual creatures, is one of the main goals of artificial life. Lenia, a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states, has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures and display complex behaviors. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. Furthermore, each of these creatures exist only in worlds governed by specific update rules and thus cannot interact in the same one. This paper proposes as mass-conservative extension of Lenia, called Flow Lenia, that solve both of these issues. We present experiments demonstrating its effectiveness in generating SLPs with complex behaviors and show that the update rule parameters can be optimized to generate SLPs showing behaviors of interest. Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs.

Related papers

• Unbounded: A Generative Infinite Game of Character Life Simulation [68.37260000219479]
  We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. We leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models.
  arXiv  Detail & Related papers  (2024-10-24T17:59:31Z)
• LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata [0.0]
  We show that a decoder-only generative pretrained transformer (GPT) model can simulate Conway's Game of Life (Life) on a toroidal grid with no prior knowledge on the size of the grid. Our results pave the path towards true universal computation within a large language model framework.
  arXiv  Detail & Related papers  (2024-09-03T11:43:16Z)
• Toward Artificial Open-Ended Evolution within Lenia using Quality-Diversity [5.380545611878407]
  We show that Quality-Diversity is an effective framework for the automatic discovery of diverse self-organizing patterns in complex systems. Our framework, called Leniabreeder, can leverage both manually defined diversity criteria and unsupervised measures of diversity to broaden the scope of discoverable patterns.
  arXiv  Detail & Related papers  (2024-06-06T16:35:27Z)
• RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning [75.61681328968714]
  We propose recurrent independent Grid LSTM (RigLSTM) to exploit the underlying modular structure of the target task. Our model adopts cell selection, input feature selection, hidden state selection, and soft state updating to achieve a better generalization ability.
  arXiv  Detail & Related papers  (2023-11-03T07:40:06Z)
• Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs [50.25683648762602]
  We introduce Koopman VAE, a new generative framework that is based on a novel design for the model prior. Inspired by Koopman theory, we represent the latent conditional prior dynamics using a linear map. KoVAE outperforms state-of-the-art GAN and VAE methods across several challenging synthetic and real-world time series generation benchmarks.
  arXiv  Detail & Related papers  (2023-10-04T07:14:43Z)
• Amorphous Fortress: Observing Emergent Behavior in Multi-Agent FSMs [3.620115940532283]
  We introduce a system called Amorphous Fortress -- an abstract, yet spatial, open-ended artificial life simulation. In this environment, the agents are represented as finite-state machines (FSMs) which allow for multi-agent interaction within a constrained space. This environment was designed to explore the emergent AI behaviors found implicitly in simulation games such as Dwarf Fortress or The Sims.
  arXiv  Detail & Related papers  (2023-06-22T19:32:53Z)
• 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)
• Stochastic Coherence Over Attention Trajectory For Continuous Learning In Video Streams [64.82800502603138]
  This paper proposes a novel neural-network-based approach to progressively and autonomously develop pixel-wise representations in a video stream. The proposed method is based on a human-like attention mechanism that allows the agent to learn by observing what is moving in the attended locations. Our experiments leverage 3D virtual environments and they show that the proposed agents can learn to distinguish objects just by observing the video stream.
  arXiv  Detail & Related papers  (2022-04-26T09:52:31Z)
• A Deep Generative Artificial Intelligence system to decipher species coexistence patterns [0.0]
  We explore cutting-edge Machine Learning techniques to decipher species coexistence patterns in vegetation patches. The GAN accurately reproduces the species composition of real patches as well as the affinity of plant species to different soil types. By reconstructing successional trajectories we could identify the pioneer species with larger potential to generate a high diversity of distinct patches.
  arXiv  Detail & Related papers  (2021-07-13T12:12:11Z)
• Hyperspace Neighbor Penetration Approach to Dynamic Programming for Model-Based Reinforcement Learning Problems with Slowly Changing Variables in A Continuous State Space [58.720142291102135]
  We introduce a Hyperspace Neighbor Penetration (HNP) approach that solves the problem of handling slowly changing variables in reinforcement learning. HNP captures in each transition step the state's partial "penetration" into its neighboring hyper-tiles in the gridded hyperspace. In summary, HNP can be orders of magnitude more efficient than classical method in handling slowly changing variables in reinforcement learning.
  arXiv  Detail & Related papers  (2021-06-10T04:58:31Z)
• Neural Production Systems [90.75211413357577]
  Visual environments are structured, consisting of distinct objects or entities. To partition images into entities, deep-learning researchers have proposed structural inductive biases. We take inspiration from cognitive science and resurrect a classic approach, which consists of a set of rule templates. This architecture achieves a flexible, dynamic flow of control and serves to factorize entity-specific and rule-based information.
  arXiv  Detail & Related papers  (2021-03-02T18:53:20Z)

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.