Despite significant progress in AI and decision-making technologies in safety-critical fields, challenges remain in verifying the correctness of decision output schemes and verification-result driven design. We propose correctness learning (CL) to enhance human-AI collaboration integrating deductive verification methods and insights from historical high-quality schemes. The typical pattern hidden in historical high-quality schemes, such as change of task priorities in shared resources, provides critical guidance for intelligent agents in learning and decision-making. By utilizing deductive verification methods, we proposed patten-driven correctness learning (PDCL), formally modeling and reasoning the adaptive behaviors-or 'correctness pattern'-of system agents based on historical high-quality schemes, capturing the logical relationships embedded within these schemes. Using this logical information as guidance, we establish a correctness judgment and feedback mechanism to steer the intelligent decision model toward the 'correctness pattern' reflected in historical high-quality schemes. Extensive experiments across multiple working conditions and core parameters validate the framework's components and demonstrate its effectiveness in improving decision-making and resource optimization.

本研究解决了在安全关键领域中，决策输出方案和验证结果驱动设计的正确性验证的挑战。提出的正确性学习（CL）整合了演绎验证方法和历史高质量方案的见解，通过模型化和推理系统代理的自适应行为，建立了一种反馈机制以增强人机协作。实验证明，该框架在改善决策和资源优化方面表现出色。

正确性学习：基于演绎验证指导的人工智能与人类协作学习