构建类似人类的人工智能:在动态环境中模拟人类决策的通用认知算法
1Dynamic Decision Making Laboratory, Social and Decision Sciences Department, Carnegie Mellon University.
概括
人工智能 (AI) 研究最初旨在研究类似人类的行为. 这项研究探讨了当前人工智能是否通过基于实例的学习理论专注于动态决策,实现了这一目标,确定了未来类似人类的人工智能发展的差距.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算心理学 计算心理学
背景情况:
- 早期的人工智能 (AI) 追求了类似人类的行为,旨在实现无法区分的性能.
- 当前的人工智能往往优先考虑在特定任务中优于人类的表现,而不是复制人类的认知.
- 人工智能的初始目标与目前的发展轨迹之间存在分歧.
研究的目的:
- 调查计算算法是否已经实现了初始的AI目标,即表现出类似人类的行为.
- 从计算认知科学的角度探讨这个问题,专注于动态决策.
- 评估人工智能在模拟人类决策过程中的现状.
主要方法:
- 介绍了一种通用认知算法,旨在模拟动态环境中的人类决策.
- 使用基于实例的学习理论 (IBLT) 来构建证据的讨论.
- 分析现有研究,以评估当前人工智能决策机制与人类的相似性.
主要成果:
- 根据IBLT认知步骤,确定支持某些AI决策机制与人类相似的证据.
- 突出显著的研究差距,阻碍了人类决策过程的高可靠性计算模型的开发.
- 表明,虽然取得了一些进展,但人工智能尚未完全实现类似人类行为的初始目标.
结论:
- 当前的计算算法在动态决策中表现出部分的人类相似性,特别是当它们以IBLT等理论为指导时.
- 需要大量的研究来弥合当前人工智能能力和真正的人类认知模拟之间的差距.
- 未来的人工智能开发应该专注于推进表现出类似人类行为的算法,以更好地支持人类决策.
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