Logical Human-Computer Interaction Audit Methodology

This dataset/resource package, Logical Human-Computer Interaction Audit Methodology, addresses the scenario of ‘logically auditable writing and reasoning verification in human-computer collaboration’. It systematically provides a reproducible, traceable set of interaction principles and a formalised audit framework: AI formalises natural language arguments into logical structures, performs exhaustive verification, identifies counterexamples, and generates proofs/models. The materials explicitly mandate that all conclusions must be traceable to the premise set Γ, conclusion φ, and sequence of inference rules. Semantic annotations distinguish between ‘derivability’ and ‘semantic implication,’ while auditing risks associated with contradictory pairs (φ and ¬φ) and the ‘explosion principle.’ Consistency metrics including PCR/WCR, UNSAT, and MUS are provided. Content spans traditional formal logic, propositional and first-order logic, proof theory and model theory, computability and non-classical logic modules. It includes a reasoning rules repository, common fallacy database, quantified contradiction audit metrics, end-to-end workflow, and a directly reusable prompt repository with appendix cross-reference tables. This facilitates immediate deployment and reuse in academic writing, policy text auditing, knowledge base consistency governance, and automated reasoning systems.