讲座预告｜沈榆平：From logic to learning: A computational complexity perspective

❍讲座摘要：Logic and learning can be viewed as duals of forward and backward reasoning. A key challenge in both fields is identifying problems that are computationally hard to infer or learn. Currently, explicit hardness for propositional logic remains open, and most hardness results in learning theory rely on unproven complexity assumptions. This talk provides a computational overview of these theories and discusses recent progress and conjectures aimed at overcoming these limitations via nonmonotonic propositional logic.

❍讲者简介：沈榆平，中山大学逻辑与认知研究所教授、博导，哲学系副主任。主要研究方向为逻辑与计算，知识表示与推理，代表性成果发表于ACM Transactions on Computational Logic，AAAI，KR等国际权威期刊与会议。

海报｜刘欣

编辑｜刘烨童

初核｜胡扬

复核｜伍素

终审｜熊明