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English(EN) Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila

AI智能体利用内在动机模仿果蝇自主性

研究人员通过创建具有内在动机的人工智能智能体,模仿果蝇(Drosophila melanogaster)的自发探索行为,开发了一个理解动物自主性的新框架。通过结合果蝇的全脑记录和数字孪生,该研究测试了各种内在动机算法,包括基于好奇心和能力的方法。DIAYN算法侧重于最大化行为熵和诱导的感官观测的可分离性,在匹配果蝇的运动统计数据和它们对未来行动的神经表征方面表现出最强的性能。 AI

影响 为逆向工程动物自主性提供了一个新颖的计算框架,并开发更复杂的具有内在动机的AI智能体。

排序理由 学术论文,详细介绍了理解动物自主性的新计算框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AI智能体利用内在动机模仿果蝇自主性

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学术论文,详细介绍了理解动物自主性的新计算框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Declan Kutscher, Reece Keller, Alyn Kirsch, Felix Pei, Aran Nayebi ·

    自主代理的内在目标:分层探索捕捉果蝇未来的意愿行为

    arXiv:2610.34638v1 Announce Type: cross Abstract: Animals have a remarkable capacity for autonomy---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind this ability largely remain a mystery. We combine wh…