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The Animal-AI Testbed and Competition

Academic Journal article by Matthew McGill, Benjamin Beyret, Murray Shanahan, José Hernández-Orallo, Lucy Cheke, Marta Halina

The Animal-AI Testbed and CompetitionProceedings of Machine Learning Research 123:164–176, 2020 NeurIPS 2019 Competition and Demonstration Track. 

Editors: Hugo Jair Escalante and Raia Hadsell

Abstract: 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.

More information about the project can be found on the Animal-AI Olympics webpage.

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