{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-animal-ai-environment-training-and","title":"The Animal-AI Environment: Training and Testing Animal-Like Artificial Cognition","arxiv_id":"1909.07483","date":"2019-09-12","proceeding":null,"authors":["Benjamin Beyret","José Hernández-Orallo","Lucy Cheke","Marta Halina","Murray Shanahan","Matthew Crosby"],"abstract":"Recent advances in artificial intelligence have been strongly driven by the use of game environments for training and evaluating agents. Games are often accessible and versatile, with well-defined state-transitions and goals allowing for intensive training and experimentation. However, agents trained in a particular environment are usually tested on the same or slightly varied distributions, and solutions do not necessarily imply any understanding. If we want AI systems that can model and understand their environment, we need environments that explicitly test for this. Inspired by the extensive literature on animal cognition, we present an environment that keeps all the positive elements of standard gaming environments, but is explicitly designed for the testing of animal-like artificial cognition.","url_abs":"https://arxiv.org/abs/1909.07483v2","url_pdf":"https://arxiv.org/pdf/1909.07483v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-animal-ai-environment-training-and","repo_url":"https://github.com/beyretb/AnimalAI-Olympics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"the-animal-ai-environment-training-and","repo_url":"https://github.com/addy369/AnimalAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"the-animal-ai-environment-training-and","repo_url":"https://github.com/gyutaaa/gyutaekim-2016011728","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"the-animal-ai-environment-training-and","repo_url":"https://github.com/ivanfeliciano/AnimalAI-INAOE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1909.07483","atlas_url":"https://app.syntology.ai/?focus=1909.07483","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}