Mechanical Self-replication

• URL: http://arxiv.org/abs/2407.14556v2
• Date: Mon, 30 Sep 2024 19:00:55 GMT
• Title: Mechanical Self-replication
• Authors: Ralph P. Lano,
• Abstract summary: This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells. The model decomposes self-replication into core components, each of which is executed by a single machine constructed from a set of basic block types.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells and supported by computer simulations. The model decomposes self-replication into core components, each of which is executed by a single machine constructed from a set of basic block types. Key functionalities such as sorting, copying, and building, are demonstrated. The model provides valuable insights into the constraints of self-replicating systems. The discussion also addresses the spatial and timing behavior of the system, as well as its efficiency and complexity. This work provides a foundational framework for future studies on self-replicating mechanisms and their information-processing applications.

Related papers

• Testing the Machine Consciousness Hypothesis [0.6426115997581661]
  The Machine Consciousness Hypothesis states that consciousness is a substrate-free functional property of computational systems.<n>I propose a research program to investigate this idea in silico by studying how collective self-models emerge from distributed learning systems.
  arXiv  Detail & Related papers  (2025-11-30T21:05:48Z)
• Fundamentals of Building Autonomous LLM Agents [64.39018305018904]
  This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs)<n>The research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities.
  arXiv  Detail & Related papers  (2025-10-10T10:32:39Z)
• Rethinking Self-Replication: Detecting Distributed Selfhood in the Outlier Cellular Automaton [0.0]
  Spontaneous self-replication in cellular automata has long been considered rare.<n>We present formal, causal evidence that such replication can emerge unassisted.
  arXiv  Detail & Related papers  (2025-08-11T14:49:11Z)
• Learning Causal Structure Distributions for Robust Planning [53.753366558072806]
  We find that learning the functional relationships while accounting for the uncertainty about the structural information leads to more robust dynamics models.<n>This in contrast with common model-learning methods that ignore the causal structure and fail to leverage the sparsity of interactions in robotic systems.<n>We show that our model can be used to learn the dynamics of a robot, which together with a sampling-based planner can be used to perform new tasks in novel environments.
  arXiv  Detail & Related papers  (2025-08-08T22:43:17Z)
• Imperfect Knowledge Management -- A Case Study in a Chilean Manufacturing Company [0.0]
  Maturana and Varela (1969) at the University of Chile introduced the term autopoiesis (from Greek self and production) This thesis introduces the Fuzzy Autopoietic Knowledge Management (FAKM) model, which integrates the system theory of living systems, the cybernetic theory of viable systems, and the autopoiesis theory of autopoietic systems.
  arXiv  Detail & Related papers  (2025-01-31T11:48:38Z)
• Self-Replicating Mechanical Universal Turing Machine [0.0]
  This paper presents the implementation of a self-replicating finite-state machine (FSM) and a self-replicating Turing Machine (TM) using bio-inspired mechanisms.
  arXiv  Detail & Related papers  (2024-09-27T08:28:37Z)
• Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks [19.574270595733502]
  We analyze the mechanisms that emerge within a vanilla attention-only Transformer trained on a simple sequence modeling task. We find that, as a result of training, the self-attention mechanism within the Transformer specializes in a way that mirrors the input and output gating mechanisms.
  arXiv  Detail & Related papers  (2024-02-13T04:28:43Z)
• Binding Dynamics in Rotating Features [72.80071820194273]
  We propose an alternative "cosine binding" mechanism, which explicitly computes the alignment between features and adjusts weights accordingly. This allows us to draw direct connections to self-attention and biological neural processes, and to shed light on the fundamental dynamics for object-centric representations to emerge in Rotating Features.
  arXiv  Detail & Related papers  (2024-02-08T12:31:08Z)
• Generative Models as a Complex Systems Science: How can we make sense of large language model behavior? [75.79305790453654]
  Coaxing out desired behavior from pretrained models, while avoiding undesirable ones, has redefined NLP. We argue for a systematic effort to decompose language model behavior into categories that explain cross-task performance.
  arXiv  Detail & Related papers  (2023-07-31T22:58:41Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning [114.36124979578896]
  We design a dynamic mechanism using offline reinforcement learning algorithms. Our algorithm is based on the pessimism principle and only requires a mild assumption on the coverage of the offline data set.
  arXiv  Detail & Related papers  (2022-05-05T05:44:26Z)
• Physical Neural Cellular Automata for 2D Shape Classification [6.709708322509072]
  Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries. We present a simple modular 2D robotic system that can infer its own class of shape through the local communication of its components.
  arXiv  Detail & Related papers  (2022-03-14T23:18:13Z)
• Towards a Predictive Processing Implementation of the Common Model of Cognition [79.63867412771461]
  We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory. The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
  arXiv  Detail & Related papers  (2021-05-15T22:55:23Z)
• Extracting a Knowledge Base of Mechanisms from COVID-19 Papers [50.17242035034729]
  We pursue the construction of a knowledge base (KB) of mechanisms. We develop a broad, unified schema that strikes a balance between relevance and breadth. Experiments demonstrate the utility of our KB in supporting interdisciplinary scientific search over COVID-19 literature.
  arXiv  Detail & Related papers  (2020-10-08T07:54:14Z)
• Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning [0.0]
  We propose a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.
  arXiv  Detail & Related papers  (2020-04-11T21:02:45Z)
• A Machine Consciousness architecture based on Deep Learning and Gaussian Processes [0.0]
  We propose an architecture that may arise consciousness in a machine based on the global workspace theory. This architecture is based on processes that use the recent developments in artificial intelligence models which output are these correlated activities.
  arXiv  Detail & Related papers  (2020-02-02T23:18:17Z)

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.