Towards the Neuroevolution of Low-level Artificial General Intelligence

• URL: http://arxiv.org/abs/2207.13583v1
• Date: Wed, 27 Jul 2022 15:30:50 GMT
• Title: Towards the Neuroevolution of Low-level Artificial General Intelligence
• Authors: Sidney Pontes-Filho, Kristoffer Olsen, Anis Yazidi, Michael A. Riegler, P{\aa}l Halvorsen and Stefano Nichele
• Abstract summary: We argue that the search for Artificial General Intelligence (AGI) should start from a much lower level than human-level intelligence. Our hypothesis is that learning occurs through sensory feedback when an agent acts in an environment. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions.
• Score: 5.2611228017034435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we argue that the search for Artificial General Intelligence (AGI) should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence (NAGI), a framework for low-level AGI. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.

Related papers

• Life, uh, Finds a Way: Systematic Neural Search [2.163881720692685]
  We tackle the challenge of rapidly adapting an agent's behavior to solve continuous problems in settings. Instead of focusing on deep reinforcement learning, we propose viewing behavior as the physical manifestation of a search procedure. We describe an algorithm that implicitly enumerates behaviors by regulating the tight feedback loop between execution of behaviors and mutation of the graph.
  arXiv  Detail & Related papers  (2024-10-02T09:06:54Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Generative Adversarial Neuroevolution for Control Behaviour Imitation [3.04585143845864]
  We propose to explore whether deep neuroevolution can be used for behaviour imitation on popular simulation environments. We introduce a simple co-evolutionary adversarial generation framework, and evaluate its capabilities by evolving standard deep recurrent networks. Across all tasks, we find the final elite actor agents capable of achieving scores as high as those obtained by the pre-trained agents.
  arXiv  Detail & Related papers  (2023-04-03T16:33:22Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Neuromorphic Computing and Sensing in Space [69.34740063574921]
  Neuromorphic computer chips are designed to mimic the architecture of a biological brain. The emphasis on low power and energy efficiency of neuromorphic devices is a perfect match for space applications.
  arXiv  Detail & Related papers  (2022-12-10T07:46:29Z)
• From Biological Synapses to Intelligent Robots [0.0]
  Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence. The potential for adaptive learning and control without supervision is brought forward. The insights collected here point toward the Hebbian model as a choice solution for intelligent robotics and sensor systems.
  arXiv  Detail & Related papers  (2022-02-25T12:39:22Z)
• A neural net architecture based on principles of neural plasticity and development evolves to effectively catch prey in a simulated environment [2.834895018689047]
  A profound challenge for A-Life is to construct agents whose behavior is 'life-like' in a deep way. We propose an architecture and approach to constructing networks driving artificial agents, using processes analogous to the processes that construct and sculpt the brains of animals. We think this architecture may be useful for controlling small autonomous robots or drones, because it allows for a rapid response to changes in sensor inputs.
  arXiv  Detail & Related papers  (2022-01-28T05:10:56Z)
• 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)
• Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning [3.8073142980733]
  In meta-learning, networks are trained with external algorithms to learn tasks that require acquiring, storing and exploiting unpredictable information for each new instance of the task. Here we evolve neural networks, endowed with plastic connections, over a sizable set of simple meta-learning tasks based on a neuroscience modelling framework. The resulting evolved network can automatically acquire a novel simple cognitive task, never seen during training, through the spontaneous operation of its evolved neural organization and plasticity structure.
  arXiv  Detail & Related papers  (2021-12-16T03:18:01Z)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)

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.