Boosting human Insight by Cooperative AI: Foundations of Shannon-Neumann Logic
artificial general intelligence, complexity, cooperative learning games, frame drift problem
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
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