Boosting human Insight by Cooperative AI: Foundations of Shannon-Neumann Logic
We present the logical foundation of an artificial intelligence (AI) capable of dealing with complex dynamic challenges, that would be very hard to handle using traditional approaches (e.g. predicate logic and deep learning). The AI is based on a cooperative questioning game, to boost insight. Insight gains are measured by information, probability, uncertainty (Shannon), as well as utility (von Neumann). The framework is a two-person cooperative iterated Q&A game, in which both players (human, AI agent) benefit (positive-sum): the human player gains insight and the AI player learns to improve itssuggestions. Generally speaking, valuable insight is typically gained by asking ’good’ questions about the ’right’ topic, at the ’appropriate’ time and place: by posing insightful questions. In this study, we propose a logical and mathematical framework, for the meanings of ’good, right, appropriate’, within clearly-defined classes of human intentions. AI based on this Shannon-Neumann Logic, combines symbolic AI with cooperative learning. It is transparent (no hidden layers), explainable (no unjustifiable moves), and remains human-aligned (no AI vs human contradictions) because of continuous cooperation (positive-sum game). In this paper, we focus uniquely on logical validity, and leave the complex topic scientific soundness for future research.
