来自像素的以对象为中心的原象形态行为推理
Ruben van Bergen1, Justus Hübotter1, Alma Lago2
1Donders Institute, Radboud University, Nijmegen, the Netherlands.
概括
这项研究介绍了一种由大脑启发的深度学习模型,该模型使用对象中心表示来表示自主代理. 该模型学会在没有人类监督的情况下推理和控制其环境,展示了新兴的逻辑推理能力.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自主代理需要桥接低级感官输入和高级推理.
- 需要无监督学习方法来避免昂贵的数据注释.
- 以对象为中心的表征提供了感知,行动和推理之间的有希望的接口.
研究的目的:
- 为自主代理提供一种新的,由大脑启发的深度学习架构.
- 为了使代理从像素中学习,使用以对象为中心的表示来解释,控制和推理.
- 在需要逻辑推理和持续控制的合成环境中证明这种方法的实用性.
主要方法:
- 开发了一种由大脑启发的新型深度学习架构.
- 采用从像素数据中学到的以对象为中心的表示.
- 使用合成环境 (2D和3DdSprites) 来进行任务评估.
主要成果:
- 代理人学会了新兴的条件行为推理和逻辑构成.
- 代理人成功地根据推断的逻辑规则控制了它的环境.
- 代理商展示了在线适应环境变化和对世界模式违规行为的稳定性.
结论:
- 以对象为中心的表示是自主代理人无监督学习的关键诱导偏差.
- 拟议的架构通过操纵接地对象表示来促进行为推理.
- 未来的工作可以将这种方法扩展到更复杂的现实世界的场景.
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