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 • The IQ-game drives a parallel (mostly right-hemispheric) non-conscious pro- cess, for gaining insights leading to an ’aha’ moment. Largely non-conscious processing can be used, where the first process proves too slow or impossible (task is too broad, ill-defined and complex). • The IQ-game is driven by dual goals: minimizing obstacles and maximizing solution qualities. The minimizing questions guide H to eliminate or reduce difficulties in the problem, when possible. The maximizing questions guide H to boost specific solution qualities, when constraints allow it. It is a dynamic optimization (changes with H ’s understanding). We discuss this process in section 3.4. • The IQ-game provides a non-brittle reasoning framework , which continuously adapts to the human player H ’s cognitive intentions C . This mindset C evolves as H ’s understanding of the challenge progresses. The IQ-game copes with the framework drift problem (section 2.3). For the AI-agent, A SN , the IQ-game has these roles: • The IQ game produces game session episodes, from which the agent A SN can learn via cooperative learning. • The IQ game ensures the agent remains human-aligned [10], because of the continuous human judgments. What is useful, informative, insightful for a human player H , does not necessarily mean the same for A SN , even if it starts that way. In the learning process, these values can drift apart, due to many factors. In the IQ game, human valuation is the ultimate arbiter , for the insight value of a question (since any AI short of a full AGI super- intelligence, will fail miserably at this task), while SN-Logic estimates the insight values, given C ( t ) . • The IQ game taps into a most valuable human resource: our collective evidence-based knowledge, undeniably our greatest accomplishment (culture, science, technology). Note that our collective belief-based human selections are often poor (e.g. who we put in power as our leader). The forces here are complex and evolutionary: desire for control, cognitive biases and herd mentality from the fear of social isolation (e.g. [11]). These factors are absent in the IQ procedure, since decisions are individual, and based directly on one’s own experience of a question’s insight, within a very specific cognitive context C ( t ) . It uses direct evidence-based judgment, where H ’s main incentive is to make life easier for herself. There are, of course individual variations in the experienced insightfulness of questions, but only stable patterns (across many individuals) are retained in cooperative learning (not presented in this paper). A complex challenge is typically time-evolving, multi-objective, multi-solution, multi- discipline, multi-level and open-ended, making it hard from the start, to clearly define a single problem, even when it is urgent (e.g. a crisis) or critical (e.g. sus- tainability), or both (e.g. a pandemic) Instead, there is a drift in the framing of problem and its solutions, as we accumulate new insights about a challenge: a framework drift problem. The drift cannot be handled with a static AI/ML system, focused on a given narrow problem. The IQ-game, copes with the framework drift, by using an adaptive reasoning framework , and an adaptive cognitive intention C = { framework, where, when, what } 1 Year 2022 3 © 2022 Global Journals Global Journal of Science Frontier Research Volume XXII Issue ersion I V IV ( F ) b) IQ-game: AI player perspective c) Framework drift problem 10. Russell S. (2019) Human Compatible: Artificial Intelligence and the Problem of Control, Viking, New York. R ef