Lawless William F
Departments of Mathematics & Psychology, Paine College, Augusta, GA 30901, USA.
Summer Fellow 2020, Naval Research Laboratory, Washington, DC 20375, USA.
Entropy (Basel). 2020 Oct 28;22(11):1227. doi: 10.3390/e22111227.
As humanity grapples with the concept of autonomy for human-machine teams (A-HMTs), unresolved is the necessity for the control of autonomy that instills trust. For non-autonomous systems in states with a high degree of certainty, rational approaches exist to solve, model or control stable interactions; e.g., game theory, scale-free network theory, multi-agent systems, drone swarms. As an example, guided by artificial intelligence (AI, including machine learning, ML) or by human operators, swarms of drones have made spectacular gains in applications too numerous to list (e.g., crop management; mapping, surveillance and fire-fighting systems; weapon systems). But under states of uncertainty or where conflict exists, rational models fail, exactly where interdependence theory thrives. Large, coupled physical or information systems can also experience synergism or dysergism from interdependence. Synergistically, the best human teams are not only highly interdependent, but they also exploit interdependence to reduce uncertainty, the focus of this work-in-progress and roadmap. We have long argued that interdependence is fundamental to human autonomy in teams. But for A-HMTs, no mathematics exists to build from rational theory or social science for their design nor safe or effective operation, a severe weakness. Compared to the rational and traditional social theory, we hope to advance interdependence theory first by mapping similarities between quantum theory and our prior findings; e.g., to maintain interdependence, we previously established that boundaries reduce dysergic effects to allow teams to function (akin to blocking interference to prevent quantum decoherence). Second, we extend our prior findings with case studies to predict with interdependence theory that as uncertainty increases in non-factorable situations for humans, the duality in two-sided beliefs serves debaters who explore alternatives with tradeoffs in the search for the best path going forward. Third, applied to autonomous teams, we conclude that a machine in an A-HMT must be able to express itself to its human teammates in causal language however imperfectly.
随着人类努力应对人机团队（A-HMT）的自主性概念，灌输信任的自主性控制的必要性仍未得到解决。对于处于高度确定性状态的非自主系统，存在理性方法来解决、建模或控制稳定的交互；例如，博弈论、无标度网络理论、多智能体系统、无人机群。例如，在人工智能（AI，包括机器学习，ML）或人类操作员的引导下，无人机群在众多应用中取得了惊人进展，不胜枚举（例如，作物管理；测绘、监视和消防系统；武器系统）。但在不确定性状态或存在冲突的情况下，理性模型会失效，而这正是相互依存理论蓬勃发展之处。大型的耦合物理或信息系统也可能因相互依存而产生协同或反协同效应。协同地，最佳的人类团队不仅高度相互依存，而且还利用相互依存来减少不确定性，这是这项正在进行的工作和路线图的重点。我们长期以来一直认为，相互依存是团队中人类自主性的基础。但对于A-HMT而言，没有数学方法可以基于理性理论或社会科学来进行其设计或安全有效的操作，这是一个严重的弱点。与理性和传统社会理论相比，我们希望首先通过描绘量子理论与我们先前发现之间的相似性来推进相互依存理论；例如，为了维持相互依存，我们先前确定边界会减少反协同效应，使团队能够发挥作用（类似于阻止干扰以防止量子退相干）。其次，我们通过案例研究扩展先前的发现，用相互依存理论预测，随着人类在不可分解情况下的不确定性增加，双边信念中的二元性对那些在寻找前进最佳路径时探索权衡取舍的替代方案的辩论者有利。第三，应用于自主团队，我们得出结论，A-HMT中的机器必须能够用因果语言向其人类队友表达自己，无论多么不完善。
