35 lines
2.6 KiB
Plaintext
35 lines
2.6 KiB
Plaintext
---
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title: "Tasked Self-Replication"
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tags: [artificial-life, complex-systems, neural-networks, self-organization, multi-task-learning, self-replication, artificial-chemistry, evolution, computational-systems, guided-evolution, artificial-intelligence]
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excerpt: "Self-replicating networks perform tasks, exploring stabilization in artificial chemistry."
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teaser: "/figures/13_sr_teaser.jpg"
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venue: "ALIFE 2021"
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---
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Building upon the concept of self-replicating neural networks <Cite bibtexKey="gabor2019self" />, this research explores the integration of **auxiliary functional goals** alongside the primary objective of self-replication. The aim is to create networks that can not only reproduce their own weights but also perform useful computations or interact meaningfully with an environment simultaneously.
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<CenteredImage
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src="/figures/13_sr_analysis.jpg"
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alt="Analysis graphs or visualizations related to dual-task self-replicating networks"
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width={800}
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height={600}
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caption="Analysis of networks balancing self-replication and auxiliary tasks."
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/>
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The study introduces a methodology for **dual-task training**, utilizing distinct input/output vectors to manage both the replication process and the execution of a secondary task. A key finding is that the presence of an auxiliary task does not necessarily hinder self-replication; instead, it can sometimes **complement and even stabilize** the replication dynamics.
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<FloatingImage
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src="/figures/13_sr_recovering.png"
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alt="3D plot showing trajectories of network states over timesteps with starting and ending points"
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width={400}
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height={400}
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float="right"
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caption="Visualization of network state trajectories and recovery in a transformed 3D space over timesteps, illustrating dynamic evolution."
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/>
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Further investigations were conducted within the framework of an **"artificial chemistry" environment**, where populations of these dual-task networks interact:
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* The impact of varying **action parameters** (related to the secondary task) on the collective learning or emergent behavior of the network population was examined.
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* A concept of a specially designed **"guiding particle"** network was introduced. This network influences its peers, demonstrating a mechanism for potentially steering the population's evolution towards desired goal-oriented behaviors.
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This work provides insights into how functional complexity can be integrated with self-replication in computational systems, offering potential pathways for developing more sophisticated artificial life models and exploring guided evolution within network populations. <Cite bibtexKey="gabor2021goals" /> |