“神经-符号”融合规划器性能显著超越o1：借鉴人类运动学习机制｜中国科学院磐石研发团队

Core Viewpoint - The article introduces a new "neuro-symbolic" hybrid planner developed by the Chinese Academy of Sciences, which significantly enhances the efficiency and precision of scientific research planning compared to traditional methods [1][5]. Group 1: Mechanism and Features - The hybrid planner integrates the advantages of both neural planning systems and symbolic planning systems, leading to improved expressiveness, adaptability, generalization, and interpretability [3][11]. - It employs a closed-loop feedback mechanism inspired by human motor learning, enhancing the planner's ability to detect and correct errors dynamically [10][6]. - The system features a self-control mechanism that allows the planner to determine when to receive feedback, optimizing the frequency of feedback and reducing dependency on it [18][21]. Group 2: Performance Evaluation - The hybrid planner was evaluated against eight representative planning tasks in the International Planning Competition (IPC), showing an average coverage rate of 70.81%, which is significantly higher than other comparative planners [23][25]. - In a comparison with OpenAI's o1 model on the PlanBench dataset, the hybrid planner achieved 100% coverage and significantly reduced average planning time, demonstrating its superior efficiency and effectiveness [26][25].