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

© 2022 Global Journals 1 Year 2022 2 Global Journal of Science Frontier Research Volume XXII Issue ersion I V IV ( F ) Boosting Human Insight by Cooperative AI: Foundations of Shannon-Neumann Logic • In section 1, we discussed algorithmic vs human intelligence, and the purpose of SN-Logic. • In section 2, we present the two-person (human H , AI agent A SN ) coop- erative Iterated Questioning (IQ) game’s role, from both H ’s and A SN ’s perspectives • In section 2.3, we discuss the dynamic drift problem: coping with the chang- ing human understanding of a given complex challenge, using a dynamic optimization process. It’s impossible to clearly define a single problem, in complex challenges (e.g. war on drugs) so that they can last for decades • In sections 3.1-3.2, we discuss SN-Logic’s requirements to cope with insight (which involves causality, information, logic, probability, uncertainty and utility) and the spaces over which SN-Logic operates • In sections 3.3-3.4, we introduce SN-Logic’s grammar: semantics + syntax The syntax is used by question generators , to build millions of possible ques- tions • In section 3.5, we present SN-Logic predicates of two classes: problem difficulty- minimizing, and solution quality-maximizing, used in all inferences • In section 3.6, we discuss the complexity and scope of SN-Logic, and section 3.7 highlights the distinction between knowledge acquisition (symbolic AI) and cooperative (machine) learning, both present in our AI • In section 3.8, we introduce the normal form for making SN-inferences , about a question’s insightfulness • In section 4, we introduce the Insight Gain Tensor µ ( when, where, what, which ) to select sound inferences, from the many valid normal-form inferences, and measures of insight gains associated to these questions • In section 5, we illustrate the use of SN-Logic, and we perform a validation test, to show how SN-Logic/IQ-game helps finding a solution path, to a component of a hard real-world solved case (quantum field theory research topic) The Iterated Questioning or IQ game, is described in paper I. During a game ses- sion, the AI-agent, A SN , poses the human player H , a question q ∈ Q , it thinks is most insightful, given H ’s current cognitive mindset C ( t ) . H then explores it, and reports if it was insightful. These are the game’s cooperative policies , both players agree to adopt for each Q&A episode. The game serves several purposes which benefits both players (positive-sum game) [7, 9] For the human player, H , the IQ-game has the following main roles: • The IQ-game is a Q&A process that reduces uncertainty and increases in- formation about a specific problem, via a sequence of Q&As. It provides an effective tool , to gain insight on the many aspects of a complex challenge. • The IQ-game drives a sequential (mostly left-hemispheric) conscious reason- ing for solving well-defined (narrow) tasks. This process is mirrored by al- gorithmic AI. For complex tasks, this process alone fails to deliver full so- lutions. Conceptual solutions to such problems require the next process: insight-gaining. II. T wo -P erson C ooperative IQ-G ame a) IQ-game: Human player perspective insight-gains (see paper I [8]): built by combining information, probability, uncer- tainty [6] and utility [7]. This paper is structured as follows: 7. Von Neumann, J. and Morgenstern O. (1944) Theory of Games and Economic Behavior, Princeton University Press: Princeton, NJ. R ef