融合符号推理与超图模型的几何机器证明专题讲习班

本项目计划邀请计算机代数、数学机械化、机器学习、组合图论与优化等领域的国内外专家担任“融合符号推理与超图模型的几何机器证明专题讲习班”的主讲人，围绕计算机代数与符号计算理论、机器证明、数学机械化、矩阵与多项式理论、组合图论与优化、可解释的机器学习、强化学习、图神经网络等8个专题，进行为期3周左右的教学与研讨。本项目旨在通过一系列的专题授课以及研讨，加强各高校间的学术联系。通过本次讲习班，让学员们掌握开展几何认知推理研究所需的基础理论，同时了解将机器学习、强化学习、图神经网络等归纳式学习模型应用于数学机械化的前沿研究成果，拓宽学术视野，提高学术能力。通过本专题讲习班的学习，学员们将 1）把握现代几何认知推理领域的宏观图景；2）运用机器学习模型来辅助求解几何认知推理中的一些难点步骤；3）发展几何定理与难题的可读式演绎证明新范式；从而促进人工智能和数学两大领域的融合与发展.

The proposed workshop aims to bring together leading experts in computer algebra, mathematical mechanization, machine learning, combinatorial graph theory, and optimization to serve as instructors in the field of geometric machine proof. Over a period of three weeks, participants will explore eight topics including Symbolic Computation Theory, Machine Proof, Mathematical Mechanization, Matrix and Polynomial Theory, Combinatorial Graphs and Optimization, Explainable Machine Learning, Reinforcement Learning, and Graph Neural Networks. The workshop also seeks to foster academic connections and communication between universities through lectures and academic talks. . One of the primary objectives of the workshop is to provide attendees with a foundational understanding of the theory behind geometric cognitive reasoning research. Additionally, participants will be exposed to cutting-edge applications of machine learning, reinforcement learning, graph neural networks, and other inductive learning models in the context of mathematical mechanization, thereby expanding their academic horizons and enhancing their abilities. . Through their participation in the workshop, students will be able to gain a comprehensive understanding of modern geometric cognitive reasoning, leverage machine learning models to assist in problem-solving, and develop new paradigms for readable deductive proofs on geometry theorems and high-level challenges. Ultimately, this workshop has the potential to promote the integration and development of artificial intelligence and mathematics across disciplines.