The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL Behavior Research Methods Pub Date : 2025-02-28 DOI:10.3758/s13428-025-02616-3
Konstantinos Voudouris, Ben Slater, Lucy G Cheke, Wout Schellaert, José Hernández-Orallo, Marta Halina, Matishalin Patel, Ibrahim Alhas, Matteo G Mecattaf, John Burden, Joel Holmes, Niharika Chaubey, Niall Donnelly, Matthew Crosby
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Abstract

The Animal-AI Environment is a unique game-based research platform designed to facilitate collaboration between the artificial intelligence and comparative cognition research communities. In this paper, we present the latest version of the Animal-AI Environment, outlining several major features that make the game more engaging for humans and more complex for AI systems. These features include interactive buttons, reward dispensers, and player notifications, as well as an overhaul of the environment's graphics and processing for significant improvements in agent training time and quality of the human player experience. We provide detailed guidance on how to build computational and behavioural experiments with the Animal-AI Environment. We present results from a series of agents, including the state-of-the-art deep reinforcement learning agent Dreamer-v3, on newly designed tests and the Animal-AI testbed of 900 tasks inspired by research in the field of comparative cognition. The Animal-AI Environment offers a new approach for modelling cognition in humans and non-human animals, and for building biologically inspired artificial intelligence.

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动物人工智能环境:比较认知和人工智能研究的虚拟实验室。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
期刊最新文献
Probing beyond: The impact of model size and prior informativeness on Bayesian SEM fit indices. Polytomous explanatory item response models for item discrimination: Assessing negative-framing effects in social-emotional learning surveys. Evaluating mobile-based data collection for crowdsourcing behavioral research. The Animal-AI Environment: A virtual laboratory for comparative cognition and artificial intelligence research. Correction: Assessing the distortions introduced when calculating d': A simulation approach.
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