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Artificial Life

3260 papers • 126 benchmarks • 313 datasets

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Developmental Learning

Most implemented papers

Lenia - Biology of Artificial Life

B. Chan•Wed Dec 12 2018

A new system of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule, which supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-world microscopic organisms.

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Artificial life properties of directed interaction combinators vs. chemlambda

Marius Buliga•Mon May 11 2020

The main conclusion of these experiments is that graph re writes systems which allow conflicting rewrites are better than those which don't, as concerns their artificial life properties, in contradiction with the search for good graph rewrite systems for decentralized computing.

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Melting Pot 2.0

J. Agapiou, Joel Z. Leibo, R. Comanescu, A. Vezhnevets, R. Koster, Edgar A. Duéñez-Guzmán, Jayd Matyas, Yiran Mao, P. Sunehag, Udari Madhushani, Kavya Kopparapu, D. Strouse, Michael Bradley Johanson, Sukhdeep Singh, Julia Haas, Igor Mordatch, D. Mobbs•Wed Nov 23 2022

Melting Pot 2.0 is described, which revises and expands on Melting Pot, and introduces support for scenarios with asymmetric roles, and explains how to integrate them into the evaluation protocol.

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Coevolution of Camouflage

C. Reynolds•Sun Apr 23 2023

This work simulates an abstract model of adversarial relationship between predator and prey, and looks at crypsis through evolving prey camouflage patterns in competition with evolving predator vision, to help study camouflage in nature, and the perceptual phenomenon of camouflage more generally.

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Self-Replicating Machines in Continuous Space with Virtual Physics

Peter D. Turney, Arnold G. Smith, Robert Ewaschuk•Sat Nov 30 2002

JohnnyVon is an implementation of self-replicating machines in continuous two-dimensional space that demonstrates that, if an arbitrary seed pattern is put in a soup of separate individual particles, the pattern will replicate by assembling the individual particles into copies of itself.

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Safe mutations for deep and recurrent neural networks through output gradients

J. Clune, Kenneth O. Stanley, J. Lehman, Jay Chen•Sun Dec 17 2017

A family of safe mutation (SM) operators that facilitate exploration without dramatically altering network behavior or requiring additional interaction with the environment are proposed, which dramatically increases the ability of a simple genetic algorithm-based neuroevolution method to find solutions in high-dimensional domains that require deep and/or recurrent neural networks.

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Evolving Structures in Complex Systems

Tomas Mikolov, Josef Sivic, Hugo Cisneros•Sun Nov 03 2019

This paper proposes an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata and demonstrates that using the proposed metric it can automatically construct computational models with emerging properties similar to those found in the Conway’s Game of Life.

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Combinatory Chemistry: Towards a Simple Model of Emergent Evolution

Tomas Mikolov, Germán Kruszewski•Mon Mar 16 2020

Combinatory Chemistry is introduced, an Algorithmic Artificial Chemistry based on a minimalistic computational paradigm named Combinatory Logic that discovers a wide range of emergent patterns that rely on acquiring basic constituents from the environment and decomposing them in a process that is remarkably similar to biological metabolisms.

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Lenia and Expanded Universe

B. Chan•Wed May 06 2020

Experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns, are reported, finding new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation.

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Open Questions in Creating Safe Open-ended AI: Tensions Between Control and Creativity

J. Clune, J. Lehman, Adrien Ecoffet•Thu Jun 11 2020

This paper explains how unique safety problems manifest in open-ended search, and suggests concrete contributions and research questions to explore them, to inspire progress towards creative, useful, and safe open-ending search algorithms.

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