Problem Representation

Problem representation in artificial intelligence focuses on encoding diverse problem types, such as combinatorial problems and mathematical word problems, into formats suitable for machine learning algorithms. Current research emphasizes developing generic representations, moving beyond problem-specific approaches, often utilizing graph neural networks or expression syntax trees to capture problem structure. This work aims to improve the generalizability and efficiency of AI systems across various domains, impacting fields like automated reasoning, optimization, and educational assessment by enabling more robust and adaptable solutions.