规划，又如何？

IF 19.3 1区材料科学 Q1 CHEMISTRY, PHYSICAL

ACS Energy Letters Pub Date : 2024-09-23 DOI:10.1017/S0140525X24000190

Jacques Pesnot-Lerousseau, Christopher Summerfield

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引用次数: 0

摘要

深度元学习是当代人工智能研究取得进展的推动力，也是自然智能中一种有前途的灵活认知理论。我们同意 Binz 等人的观点，即元学习比经典模型更能解释许多所谓 "基于模型 "的行为。我们认为，这促使我们重新审视问题解决和目标导向规划的神经理论。

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Quo vadis, planning?

Deep meta-learning is the driving force behind advances in contemporary AI research, and a promising theory of flexible cognition in natural intelligence. We agree with Binz et al. that many supposedly "model-based" behaviours may be better explained by meta-learning than by classical models. We argue that this invites us to revisit our neural theories of problem solving and goal-directed planning.

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来源期刊

ACS Energy Letters Energy-Renewable Energy, Sustainability and the Environment

CiteScore

31.20

自引率

5.00%

发文量

469

审稿时长

1 months

期刊介绍： ACS Energy Letters is a monthly journal that publishes papers reporting new scientific advances in energy research. The journal focuses on topics that are of interest to scientists working in the fundamental and applied sciences. Rapid publication is a central criterion for acceptance, and the journal is known for its quick publication times, with an average of 4-6 weeks from submission to web publication in As Soon As Publishable format. ACS Energy Letters is ranked as the number one journal in the Web of Science Electrochemistry category. It also ranks within the top 10 journals for Physical Chemistry, Energy & Fuels, and Nanoscience & Nanotechnology. The journal offers several types of articles, including Letters, Energy Express, Perspectives, Reviews, Editorials, Viewpoints and Energy Focus. Additionally, authors have the option to submit videos that summarize or support the information presented in a Perspective or Review article, which can be highlighted on the journal's website. ACS Energy Letters is abstracted and indexed in Chemical Abstracts Service/SciFinder, EBSCO-summon, PubMed, Web of Science, Scopus and Portico.