嵌入式人机团队的量子式相互依赖模型:审查面对复杂性和不确定性的自主道路
William F Lawless1, Ira S Moskowitz2, Katarina Z Doctor2
1Department of Mathematics and Psychology, Paine College, Augusta, GA 30901, USA.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
本综述探讨了开放系统中人工智能 (AI) 的量子类算法,通过采用相互依赖而不是传统的个人方法来优化人机团队性能.
科学领域:
- 人工智能的人工智能
- 团队科学 团队科学
- 量子类型系统 量子类型系统
背景情况:
- 传统的人机团队研究在开放系统中与复杂性,不确定性和冲突作斗争.
- 个人主义方法汇总数据,但产生有限的概括结果,并面临复制危机.
- 现有的模型缺乏关于相互依赖和开放环境的动态性质的强有力的理论.
研究的目的:
- 在开放系统中设计和测试人工智能的量子类算法.
- 在复杂,不确定的和充满冲突的环境中构建人机团队,以实现最大的性能.
- 为团队中的相互依赖提出一个新的理论框架.
主要方法:
- 在团队设计中对比个人和相互依赖的方法.
- 开发一种基于相互依赖的量子模型.
- 审查现有的团队科学和人机交互研究.
- 分析相互依存和量子式概念的数学.
主要成果:
- 量子式相互依赖模型解决了传统方法的局限性.
- 思想和身体的双重能力理论化了团队结构和表现.
- 测量问题和非可算因素性质解决了复制危机,并减少了自由度,与科学发现保持一致.
结论:
- 量子类型的算法为在开放系统中优化人机团队提供了一个新的框架.
- 相互依赖,而不是个人主义,是理解和改善团队动态的关键.
- 这种方法为推进人机协作中相互依赖和自主性的科学提供了一条前进的道路.
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