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控制的节推论用于控制
Itzel Olivos-Castillo1, Paul Schrater2,3, Xaq Pitkow1,4,5,6,7,8,9
1Department of Computer Science, Rice University.
ArXiv
|November 22, 2024
概括
人工智能需要平衡性能与资源使用,特别是在不确定的环境中. 本研究介绍了资源高效计算的框架,揭示了管理不确定性以优化性能的策略.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 对人工智能来说,平衡效用最大化与资源限制 (计算,移动) 是至关重要的.
- 在部分可观测的环境中对资源效率的理解有限.
- 现有的框架往往忽视了获取信息的成本.
研究的目的:
- 在部分可观测性下制定一个资源效率决策框架.
- 将通过推理获得的信息视为可优化的资源.
- 揭示人工智能和生物系统中资源效率的基本原则.
主要方法:
- 扩展了部分可观察的马尔科夫决策过程 (POMDP) 框架.
- 集成的信息获取作为一种资源,与任务执行和运动努力相辅相成.
- 解决了线性-高斯动态环境中的问题.
- 用两个非线性任务来说明.
主要成果:
- 发现了推理策略中的阶段过渡.
- 确定了从贝叶斯最佳推断向战略不确定性管理的转变.
- 揭示了一系列同样有效,节的策略,适应未来的目标.
- 在非线性任务中证明了适用性.
结论:
- 拟议的框架允许在不确定性条件下进行合理的,资源高效的控制.
- 节的推断策略可以提高适应能力和长期绩效.
- 提供了理解和设计大脑和机器高效计算的基础.
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