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The Animal-AI Testbed and Competition

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The Animal-AI Testbed and Competition

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dc.contributor.author Crosby, Matthew es_ES
dc.contributor.author Beyret, Benjamin es_ES
dc.contributor.author Shanahan, Murray es_ES
dc.contributor.author Hernández-Orallo, José es_ES
dc.contributor.author Cheke, Lucy es_ES
dc.contributor.author Halina, Marta es_ES
dc.date.accessioned 2021-11-05T12:51:48Z
dc.date.available 2021-11-05T12:51:48Z
dc.date.issued 2020 es_ES
dc.identifier.uri http://hdl.handle.net/10251/176140
dc.description.abstract [EN] Modern machine learning systems are still lacking in the kind of general intelligence and common sense reasoning found, not only in humans, but across the animal kingdom. Many animals are capable of solving seemingly simple tasks such as inferring object location through object persistence and spatial elimination, and navigating efficiently in out-of-distribution novel environments. Such tasks are difficult for AI, but provide a natural stepping stone towards the goal of more complex human-like general intelligence. The extensive literature on animal cognition provides methodology and experimental paradigms for testing such abilities but, so far, these experiments have not been translated en masse into an AI-friendly setting. We present a new testbed, Animal-AI, first released as part of the Animal-AI Olympics competition at NeurIPS 2019, which is a comprehensive environment and testing paradigm for tasks inspired by animal cognition. In this paper we outline the environment, the testbed, the results of the competition, and discuss the open challenges for building and testing artificial agents capable of the kind of nonverbal common sense reasoning found in many non-human animals. es_ES
dc.description.sponsorship This work was supported by the Leverhulme Centre for the Future of Intelligence, LeverhulmeTrust, under Grant RC-2015-067. es_ES
dc.language Inglés es_ES
dc.publisher JMLR es_ES
dc.relation.ispartof Proceedings of Machine Learning Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title The Animal-AI Testbed and Competition es_ES
dc.type Artículo es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Leverhulme Trust//RC2015-067/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Crosby, M.; Beyret, B.; Shanahan, M.; Hernández-Orallo, J.; Cheke, L.; Halina, M. (2020). The Animal-AI Testbed and Competition. Proceedings of Machine Learning Research. 123:164-176. http://hdl.handle.net/10251/176140 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://proceedings.mlr.press/ es_ES
dc.description.upvformatpinicio 164 es_ES
dc.description.upvformatpfin 176 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 123 es_ES
dc.identifier.eissn 2640-3498 es_ES
dc.relation.pasarela S\432149 es_ES
dc.contributor.funder Leverhulme Trust es_ES


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