Evolutionary Self-Replication as a Mechanism for Producing Artificial
Intelligence
- URL: http://arxiv.org/abs/2109.08057v5
- Date: Fri, 23 Sep 2022 20:02:59 GMT
- Title: Evolutionary Self-Replication as a Mechanism for Producing Artificial
Intelligence
- Authors: Samuel Schmidgall, Joseph Hays
- Abstract summary: Self-replication is explored as a mechanism for the emergence of intelligent behavior in modern learning environments.
Atari and robotic learning environments are re-defined in terms of natural selection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Can reproduction alone in the context of survival produce intelligence in our
machines? In this work, self-replication is explored as a mechanism for the
emergence of intelligent behavior in modern learning environments. By focusing
purely on survival, while undergoing natural selection, evolved organisms are
shown to produce meaningful, complex, and intelligent behavior, demonstrating
creative solutions to challenging problems without any notion of reward or
objectives. Atari and robotic learning environments are re-defined in terms of
natural selection, and the behavior which emerged in self-replicating organisms
during these experiments is described in detail.
Related papers
- No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots [0.24554686192257422]
We show that intelligent behavior can be created without a separate and explicit brain for robot control.
Specifically, we show that adaptive and complex behavior can be created in voxel-based virtual soft robots by using simple reactive materials.
arXiv Detail & Related papers (2024-07-23T16:20:36Z) - A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization [55.11642177631929]
Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
arXiv Detail & Related papers (2023-10-14T23:28:48Z) - On Physical Origins of Learning [0.0]
We propose that learning may have non-biological and non-evolutionary origin.
It turns out that key properties of learning can be observed, explained, and accurately reproduced within simple physical models.
arXiv Detail & Related papers (2023-07-27T19:45:19Z) - 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) - Self-mediated exploration in artificial intelligence inspired by
cognitive psychology [1.3351610617039975]
Exploration of the physical environment is an indispensable precursor to data acquisition and enables knowledge generation via analytical or direct trialing.
This work links human behavior and artificial agents to endorse self-development.
A study is subsequently designed to mirror previous human trials, which artificial agents are made to undergo repeatedly towards convergence.
Results demonstrate causality, learned by the vast majority of agents, between their internal states and exploration to match those reported for human counterparts.
arXiv Detail & Related papers (2023-02-13T18:20:44Z) - 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) - From Psychological Curiosity to Artificial Curiosity: Curiosity-Driven
Learning in Artificial Intelligence Tasks [56.20123080771364]
Psychological curiosity plays a significant role in human intelligence to enhance learning through exploration and information acquisition.
In the Artificial Intelligence (AI) community, artificial curiosity provides a natural intrinsic motivation for efficient learning.
CDL has become increasingly popular, where agents are self-motivated to learn novel knowledge.
arXiv Detail & Related papers (2022-01-20T17:07:03Z) - 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) - Perspective: Purposeful Failure in Artificial Life and Artificial
Intelligence [0.0]
I argue that failures can be a blueprint characterizing living organisms and biological intelligence.
Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.
arXiv Detail & Related papers (2021-02-24T05:43:44Z) - Embodied Intelligence via Learning and Evolution [92.26791530545479]
We show that environmental complexity fosters the evolution of morphological intelligence.
We also show that evolution rapidly selects morphologies that learn faster.
Our experiments suggest a mechanistic basis for both the Baldwin effect and the emergence of morphological intelligence.
arXiv Detail & Related papers (2021-02-03T18:58:31Z) - RoboTHOR: An Open Simulation-to-Real Embodied AI Platform [56.50243383294621]
We introduce RoboTHOR to democratize research in interactive and embodied visual AI.
We show there exists a significant gap between the performance of models trained in simulation when they are tested in both simulations and their carefully constructed physical analogs.
arXiv Detail & Related papers (2020-04-14T20:52:49Z)
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.