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Creating High-Level Components with a Generative Representation for Body-Brain Evolution

Gregory S. Hornby, Jordan B. Pollack
2002 Artificial Life  
Creating High-Level Components understand and classify different approaches to the underlying representations of evolutionary systems.  ...  One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures.  ...  genotype changes the number of high-level components in the genobot from four to six.  ... 
doi:10.1162/106454602320991837 pmid:12537684 fatcat:ewnybqpnxbb2dbys2sxwaxzwsy

Computer Creativity in the Automatic Design of Robots

Jordan B. Pollack, Gregory S. Hornby, Hod Lipson, Pablo Funes
2003 Leonardo: Journal of the International Society for the Arts, Sciences and Technology  
We then create a representation scheme that allows for the hierarchical construction of more complex components from previously defined ones for the evolution of modular designs.  ...  Using natural systems as inspiration, we coevolve both the brain and the body, simultaneously and continuously, from a simple controllable mechanism to one of sufficient complexity for a particular specialized  ...  We then create a representation scheme that allows for the hierarchical construction of more complex components from previously defined ones for the evolution of modular designs.  ... 
doi:10.1162/002409403321554170 fatcat:i6eda4ielngv5dlm42k4umts7e

Evolving embodied intelligence from materials to machines

David Howard, Agoston E. Eiben, Danielle Frances Kennedy, Jean-Baptiste Mouret, Philip Valencia, Dave Winkler
2019 Nature Machine Intelligence  
We propose Multi-Level Evolution (MLE), a bottom-up automatic process that designs robots across multiple levels and niches them to tasks and environmental conditions.  ...  body plans.  ...  Acknowledgements DH, DFK, PV and DW would like to acknowledge Active Integrated Matter, one of CSIRO's Future Science Platforms, for funding this research.  ... 
doi:10.1038/s42256-018-0009-9 fatcat:kpigz7wcqrhivfsunnfbvhcgae

Meta-brain Models: biologically-inspired cognitive agents [article]

Bradly Alicea, Jesse Parent
2022 arXiv   pre-print
Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity  ...  We propose that these issues can be addressed with a computational approach we call meta-brain models.  ...  By evolving the innate component of meta-brain models, we can create a phylogenetic representation of multiple generations.  ... 
arXiv:2109.11938v2 fatcat:cvg5yuk7rrbj5bz6yfj6rnr4ba

Three Generations of Automatically Designed Robots

Jordan B. Pollack, Hod Lipson, Gregory Hornby, Pablo Funes
2001 Artificial Life  
Over the last few years we have been trying to address this challenge through an alternative approach: Rather than trying to control an existing machine or create a general-purpose robot, we propose that  ...  The difficulties associated with designing, building and controlling robots have led their development to a stasis: applications are limited mostly to repetitive tasks with predefined behavior.  ...  The representation of a robot was a direct encoding of a list of atomic components and their connectivity.  ... 
doi:10.1162/106454601753238627 pmid:11712955 fatcat:jl4ro4burzb5rmzxojanolgpz4

Gait-learning with morphologically evolving robots generated by L-system [article]

Jie Luo, Daan Zeeuwe, Agoston E. Eiben
2021 arXiv   pre-print
With regards to the brains of the offspring, we use two methods to create them. The first one entails solely evolution which means the brain of a robot child is inherited from its parents.  ...  When controllers (brains) and morphologies (bodies) of robots simultaneously evolve, this can lead to a problem, namely the brain & body mismatch problem.  ...  Some bodies are more suitable for the brains to learn with than others. How well the brain learns can be empowered by a better body.  ... 
arXiv:2107.08249v2 fatcat:4kwondw4p5czledcdgzsnrtgzi

The Effects of Learning in Morphologically Evolving Robot Systems [article]

Jie Luo, Aart Stuurman, Jakub M. Tomczak, Jacintha Ellers, Agoston E. Eiben
2021 arXiv   pre-print
Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring.  ...  This shows that evolution is producing robots with an increasing plasticity, that is, consecutive generations are becoming better and better learners which in turn makes them better and better at the given  ...  speed levels in eight generations that required 21 generations for Evolution Only.  ... 
arXiv:2111.09851v1 fatcat:bmisswx5srbv3mm6ujxtfvtdnq

Drive for Creativity [article]

Leonid Perlovsky, Daniel Levine
2011 arXiv   pre-print
We advance a hypothesis that creativity has evolved with evolution of internal representations, possibly from amniotes to primates, and further in human cultural evolution.  ...  We identify higher, conscious levels of KI with the drive for creativity (DC) and discuss the roles of language and music, brain mechanisms involved, and experimental directions for testing the advanced  ...  In the mind hierarchy, as shown in Fig. 1 , concepts-representations at every higher level have evolved in cultural evolution for the purpose of creating a unified, general meaning of lower level concepts  ... 
arXiv:1103.2373v1 fatcat:droez7kaebdadbjhnjpfmrrwtq

The Framsticks system: versatile simulator of 3D agents and their evolution

Maciej Komosin´ski
2003 Kybernetes  
The system is a universal tool for modeling, simulating and optimizing virtual agents, with three-dimensional body and embedded control system. Simulation model is described first.  ...  Then features of the system framework are presented, with typical and potential applications.  ...  Any creature can be completely described using a low-level representation f 0, by listing all of its components and attributes.  ... 
doi:10.1108/03684920310452382 fatcat:eullm7oapnagre2g2uzki5soly

Triadic (ecological, neural, cognitive) niche construction: a scenario of human brain evolution extrapolating tool use and language from the control of reaching actions

A. Iriki, M. Taoka
2011 Philosophical Transactions of the Royal Society of London. Biological Sciences  
Human higher cognitive activity can therefore be viewed holistically as one component in a terrestrial ecosystem.  ...  The dramatic expansion of the brain that accompanied additions of new functional areas would have supported such continuous evolution.  ...  James Steele for valuable comments, and Dr Michio Tanaka and Dr Teruo Hashimoto for technical assistance.  ... 
doi:10.1098/rstb.2011.0190 pmid:22106423 pmcid:PMC3223791 fatcat:khn6qsrupvdbhp455jkvi3dzxu

A new perspective on the organization of an invertebrate brain

Letizia Zullo, Binyamin Hochner
2011 Communicative & Integrative Biology  
Acknowledgements We thank Jenny Kien for suggestions and editorial assistance.  ...  These connections create a high degree of cross-talk between the lobes but the interconnections appear much less elaborate than those in vertebrate brains. 3 The high level of centralization, together  ...  For example, many invertebrate motor systems are distributed throughout a semi-autonomous PNS, yielding 'distributed' low level motor representations.  ... 
doi:10.4161/cib.13804 pmid:21509172 pmcid:PMC3073264 fatcat:f5bk62jsnrfgljspihojq56cua

THE SAPIENT PARADOX AND THE GREAT JOURNEY: INSIGHTS FROM COGNITIVE PSYCHOLOGY, NEUROBIOLOGY, AND PHENOMENOLOGY

Atsushi IRIKI, Hiroaki SUZUKI, Shogo TANAKA, Rafael BRETAS VIEIRA, Yumiko YAMAZAKI
2021 Psychologia  
After a specific point in history, hominin evolution accelerated to a level that could not be accounted for by natural selection alone.  ...  As the hominin brain expanded during TNC-1, latent cognitive capabilities were incubated within its neural framework to be expressed with a simple rewiring among brain areas in TNC-2, a quick and inexpensive  ...  For the integration of cognitive domains in TNC-2, brain representations of the self and the body appear, again as the next stage in evolution, to play a crucial role.  ... 
doi:10.2117/psysoc.2021-b017 fatcat:ectopy7xybaezjuy4sa4ebgk2q

The Information Animal and the Super-brain

John F. Hoffecker
2011 Journal of Archaeological Method and Theory  
With syntactic language, modern humans developed a more fully integrated super-brain that mirrors the flow of information within an individual brain and exhibits the property of generativity.  ...  A bstract. Analogous to a super-organism, a super-brain is defined as a group of interacting brains that collectively exhibit at least some of the characteristics of an individual brain.  ...  The organization of information in hierarchical form with many levels and embedded components reached a threshold at which a potentially infinite variety of structures could be generated by recombination  ... 
doi:10.1007/s10816-011-9124-1 fatcat:mkmujbs4lrfy7fwh56pjf57f6i

Mind Uploading and Resurrection of Human Consciousness. Place for Science?

Vadim Astakhov
2008 NeuroQuantology  
I am using that analogy to represents the toy mind functions as a geometrical shape on information manifold. Distributed holographic representation can be created for such shape.  ...  Neuroprosthesis, brain-computer interface or brain transplant might be the candidates for such migration.  ...  Therefore, such high-level nodes and links can be unfolded.  ... 
doi:10.14704/nq.2008.6.3.181 fatcat:d7t3rasgkradhkimftaffbmmoe

On hermit crabs and humans

Michael S.C. Thomas
2013 Developmental Science  
In dyscalculia, family lineages with poorer representations of number quantities were little worse off until a certain generation had to engage these representations in a wider system for counting and  ...  Developmental psychologists have sometimes viewed this sub-field with suspicion, particularly with respect to high-level cognitionnot because there is any doubt that evolution has shaped us, but because  ... 
doi:10.1111/desc.12031 pmid:23432839 fatcat:s46i2ko2lfgvpjmp6u45ekyi3y
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