Learning human insight by cooperative AI: Shannon-Neumann measure

Abstract

A conceptually sound solution to a complex real-world challenge, is built on a solid foundation of key insights, gained by posing ‘good’ questions, at the ‘right’ times/places. If the foundation is weak, due to insufficient human insight, the resulting, conceptually flawed solution, can be very costly or impossible to correct downstream. The response to the global 2020 pandemic, by countries using just-in-time supply/production chains and fragmented health-care systems, are striking examples. Here, Artificial intelligence (AI) tools to help human insight, are of significant value. We present a computational measure of insight gains, which a cooperative AI agent can compute, by having a specific internal framework, and by observing how a human behaves. This measure enables a cooperative AI to maximally boost human insight, during an iterated questioning process—a solid foundation for solving complex open-ended challenges. It is an AI-Human insight bridge, built on Shannon entropy and von Neumann utility. Our next paper will addresses how this measure and its associated strategy, reduce a hard cooperative inverse reinforcement learning game, to simple Q-Learning, proven to converge to a near-optimal policy.