Show simple item record

dc.contributor.authorPontes Filho, Sidney
dc.contributor.authorOlsen, Kristoffer
dc.contributor.authorYazidi, Anis
dc.contributor.authorRiegler, Michael
dc.contributor.authorHalvorsen, Pål
dc.contributor.authorNichele, Stefano
dc.date.accessioned2023-01-12T12:43:58Z
dc.date.available2023-01-12T12:43:58Z
dc.date.created2022-11-14T09:34:45Z
dc.date.issued2022
dc.identifier.citationFrontiers in Robotics and AI. 2022, 9, Artikkel 17547.en_US
dc.identifier.issn2296-9144
dc.identifier.urihttps://hdl.handle.net/11250/3043057
dc.description.abstractIn this work, we argue that the search for Artificial General Intelligence should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence, a framework for low-level artificial general intelligence. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is beneficial.en_US
dc.language.isoengen_US
dc.publisherFrontiers Media S.A.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectneuroevolutionen_US
dc.subjectartificial general intelligenceen_US
dc.subjectspiking neural networken_US
dc.subjectspike-timingdependent plasticityen_US
dc.subjectHebbian learningen_US
dc.subjectweight agnostic neural networken_US
dc.subjectmetalearningen_US
dc.titleTowards the Neuroevolution of Low-level artificial general intelligenceen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2022 Pontes-Filho, Olsen, Yazidi, Riegler, Halvorsen and Nichele.en_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.volume9en_US
dc.source.journalFrontiers in Robotics and AIen_US
dc.identifier.doi10.3389/frobt.2022.1007547
dc.identifier.cristin2073211
dc.source.articlenumber17547en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal