This paper defines a new approach for augmenting human intelligence with AI for optimal goal solving. Our proposed AI, Indigo, is an acronym for Informed Numerical Decision-making through Iterative Goal-Oriented optimization. When combined with a human collaborator, we term the joint system IndigoVX, for Virtual eXpert. The system is conceptually simple. We envisage this method being applied to games or business strategies, with the human providing strategic context and the AI offering optimal, data-driven moves. Indigo operates through an iterative feedback loop, harnessing the human expert's contextual knowledge and the AI's data-driven insights to craft and refine strategies towards a well-defined goal. Using a quantified three-score schema, this hybridization allows the combined team to evaluate strategies and refine their plan, while adapting to challenges and changes in real-time.

翻译：本文定义了一种通过人工智能增强人类智能以达成最优目标求解的新方法。我们提出的AI系统Indigo，是"基于迭代目标导向优化的信息型数值决策"（Informed Numerical Decision-making through Iterative Goal-Oriented optimization）的缩写。当与人类协作者结合时，我们将其联合系统称为IndigoVX（虚拟专家）。该系统的概念架构简洁明了。我们设想该方法可应用于游戏或商业策略领域：人类提供战略背景，AI提供最优化的数据驱动行动方案。Indigo通过迭代反馈循环运作，整合人类专家的情境知识与AI的数据洞察，针对明确定义的目标进行策略制定与优化。通过量化的三评分体系，这种混合机制使联合团队能够实时评估策略、优化方案，并动态应对挑战与变化。