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 © 2022 Global Journals 1 Year 2022 14 Global Journal of Science Frontier Research Volume XXII Issue ersion I V IV ( F ) 7. Q from A SN : ’Do you want to identify a new obstacle, now? ... Note: for a complex challenge, limitless combinations of obstacles can be explored in this manner. This scenario shows how suggested questions from A SN , can replicate real- world solutions to obstacles, via a cooperative Q&A dialog. The researchers do something similar between themselves, early-on, to decide what to work on. But AI’s complementary strength, is to cover many exploration paths, which are very often overlooked, yet may be key to quality solutions. This dynamic ’human-AI’ interaction would be even more fruitful, in a group brainstorming session , where each member of the team, can select directions to explore and possible answers. We mentioned (section 3.7), that insight-gain convolution tensors and kernels, form the bridge between the SN normal form inferencing (SN-validity), and measures of insight (SN-soundness); the bridge between logic (validity) and science (soundness). Initially, the tensors µ are the AI’s ’vanilla’ core, then, learned flavors are added to it, via machine learning to optimize the core AI, to distinct challenge classes. The AI’s core will be initialized by heuristics from causality, information, logic, planning, problem-solving, and utility. These apply to all types of challenges. The tensors’ added flavor, needs to be learned using cooperative learning via a renormal- ization procedure, from the IQ-game’s episodes. The construction of the insight- gain tensors and cooperative learning will be described in future work. VI. D iscussion a) Tensor Construction & Cooperative learning 17. Zyla, P.A et al (2020) Review of Particle Physics: CKM quark-mixing matrix, Progress of Theoretical and Experimental Physics . 2020 (8): 083C01. doi: 10.1093/ ptep/ptaa104. R ef We presented the foundations of SN-Logic, designed to boost human insight, to help overcome challenges that are hard to deal with, using traditional AI (mainly, predicate logic and deep learning neural nets). This required a logic, capable of coping with the concepts necessary to measure insight-gains: causality (causes of insight gains), dynamics (adaptive reasoning frameworks), information, probability, uncertainty (Shannon) and utility (von Neumann). In this paper, we presented the following: • The two-person ( H, A SN ) cooperative IQ-game’s role from both H ’s and A SN ’s perspectives • The frame drift problem : coping with the changing understanding of a chal- lenge, using a (non-brittle) logic and optimization process, which continu- ously adapt to the current human understanding and intention • SN-Logic’s requirements to compute insightfulness (which involves causal- ity, information, logic, probability, uncertainty and utility) and the concept spaces over which SN-Logic operates (to scope the quantifiers) • SN-Logic’s grammar: semantics + syntax for posing questions q ∈ Q from a vast space of potential questions. The syntax is used by a dual question generator ( q ∈ Q min , q ∈ Q max ), from which all questions are built ( N ques = O (10 7 ) ) • SN-Logic predicates of two question classes: problem difficulty-minimizing, and solution quality-maximizing, used in all inferences • The complexity of SN-Logic, and show it’s broad scope and capability of coping with a large number of distinct challenge classes. • The SN normal-form for making valid inferences, about a question’s insight- fulness, efficiently within a vast space of possibilities b) Conclusion