Article

Division blocks and the open-ended evolution of development, form, and behavior

Authors:

Mark FeinsteinAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 316 - 323

https://doi.org/10.1145/1276958.1277019

Published: 07 July 2007 Publication History

Abstract

We present a new framework for artificial life involving physically simulated, three-dimensional blocks called Division Blocks. Division Blocks can grow and shrink, divide and form joints, exert forces on joints, and exchange resources. They are controlled by recurrent neural networks that evolve, along with the blocks, by natural selection. Division Blocks are simulated in an environment in which energy is approximately conserved, and in which all energy derives ultimately from a simulated sun via photosynthesis. In this paper we describe our implementation of Division Blocks and some of the ways that it can support experiments on the open-ended evolution of development, form, and behavior. We also present preliminary data from simulations, demonstrating the reliable emergence of cooperative resource transactions.

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Cited By

Earle STogelius JSoros L(2021)Video Games as a Testbed for Open-Ended Phenomena2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619042(1-9)Online publication date: 17-Aug-2021
https://dl.acm.org/doi/10.1109/CoG52621.2021.9619042
Tyrell Davis Q(2021)Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619011(01-08)Online publication date: 17-Aug-2021
https://dl.acm.org/doi/10.1109/CoG52621.2021.9619011
Miikkulainen RForrest S(2021)A biological perspective on evolutionary computationNature Machine Intelligence10.1038/s42256-020-00278-83:1(9-15)Online publication date: 18-Jan-2021
https://doi.org/10.1038/s42256-020-00278-8
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Index Terms

Division blocks and the open-ended evolution of development, form, and behavior
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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GECCO07

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Earle STogelius JSoros L(2021)Video Games as a Testbed for Open-Ended Phenomena2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619042(1-9)Online publication date: 17-Aug-2021
https://dl.acm.org/doi/10.1109/CoG52621.2021.9619042
Tyrell Davis Q(2021)Carle's Game: An Open-Ended Challenge in Exploratory Machine Creativity2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619011(01-08)Online publication date: 17-Aug-2021
https://dl.acm.org/doi/10.1109/CoG52621.2021.9619011
Miikkulainen RForrest S(2021)A biological perspective on evolutionary computationNature Machine Intelligence10.1038/s42256-020-00278-83:1(9-15)Online publication date: 18-Jan-2021
https://doi.org/10.1038/s42256-020-00278-8
Grbic DPalm RNajarro EGlanois CRisi S(2021)EvoCraft: A New Challenge for Open-EndednessApplications of Evolutionary Computation10.1007/978-3-030-72699-7_21(325-340)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_21
Cisneros HSivic JMikolov T(2019)Evolving Structures in Complex Systems2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002840(230-237)Online publication date: Dec-2019
https://doi.org/10.1109/SSCI44817.2019.9002840
Escalera JDoyle MMondada FGroß R(2018)Evo-Bots: A Simple, Stochastic Approach to Self-assembling Artificial OrganismsDistributed Autonomous Robotic Systems10.1007/978-3-319-73008-0_26(373-385)Online publication date: 14-Mar-2018
https://doi.org/10.1007/978-3-319-73008-0_26
Wilder BStanley K(2015)Reconciling explanations for the evolution of evolvabilityAdaptive Behavior10.1177/105971231558416623:3(171-179)Online publication date: 22-May-2015
https://doi.org/10.1177/1059712315584166
Pugh JSoros LSzerlip PStanley KEsparcia-Alcázar A(2015)Confronting the Challenge of Quality DiversityProceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation10.1145/2739480.2754664(967-974)Online publication date: 11-Jul-2015
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Haasdijk EBredeche NEiben A(2014)Combining Environment-Driven Adaptation and Task-Driven Optimisation in Evolutionary RoboticsPLoS ONE10.1371/journal.pone.00984669:6(e98466)Online publication date: 5-Jun-2014
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(2013)Performing Collective Tasks with Flagellated Bacteria Acting as Natural and Hybrid MicrorobotsHandbook of Collective Robotics10.1201/b14908-27(797-822)Online publication date: 29-May-2013
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