Global Journal of Science Frontier Research, F: Mathematics and Decision Science, Volume 22 Issue 4

Boosting Human Insight by Cooperative AI: Foundations of Shannon-Neumann Logic Edouard Siregar I. I ntroduction & M otivation 1 Year 2022 1 © 2022 Global Journals Global Journal of Science Frontier Research Volume XXII Issue ersion I V IV ( F ) Purely algorithmic AI, from Predicate Logic [1] to Deep Learning neural nets [2–4], have proven highly effective for static, well-defined, narrow problems [5]. For dy- namic, complex challenges , traditional AI becomes too ’brittle’ (fails due to inap- propriate application), and human insight is necessary to guarantee sound, human- aligned solutions. Solutions built on insufficient insight, can have deep long-lasting, human and economic consequences (e.g. conflict avoidance, war on drugs, pan- demics or climate ill-preparedness). Insight is usually gained (besides randomness and serendipity), by knowing when/where to pose which types of questions, about what topic: that is, by posing ’insightful questions’ . This ability thus requires a precise logical and mathematical meaning for the variables { when, where, what, which } , within well-defined contexts C , of human cognitive mindsets . In this paper, the task of generating insightful questions, uses a framework we call Shannon-Neumann or SN-Logic, to cope with the fundamental concepts in Abstract- We present the logical foundation of an artificial intelligence (AI) capable of 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 its suggestions. 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. Keywords: artificial general intelligence, complexity, cooperative learning games, frame drift problem, information entropy, insight problems, predicate logic, renormalization, utility, value-alignment problem. 1. Leon Sterling L. and Ehud Shapiro E. (1986) The Art of Prolog: Advanced Programming Techniques (MIT Press Series in Logic Programming), MIT Press; First Ed., ISBN-10: 0262192500 ISBN-13: 978-0262192507 R ef Author: Sofia Labs, AI systems research, Maryland, USA. e-mail: edsiregar@SofiaLabsLLC.com dealing with complex dynamic challenges, that would be very hard to handle using