IndigoVX: Where Human Intelligence Meets AI for Optimal Decision Making

TL;DR

IndigoVX is a hybrid human-AI system designed to enhance decision-making in strategic contexts by combining human expertise with AI-driven optimization in an iterative, goal-oriented framework.

Contribution

The paper introduces IndigoVX, a novel hybrid system that integrates human strategic input with AI optimization for improved decision-making.

Findings

Effective iterative feedback loop for strategy refinement

Enhanced decision quality through hybrid human-AI collaboration

Real-time adaptation to changing challenges

Abstract

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.