Model Counting

Model counting, the task of determining the number of solutions to a Boolean formula, is a fundamental problem across computer science with applications ranging from probabilistic reasoning to image analysis. Current research focuses on improving the scalability and accuracy of model counters, exploring techniques like knowledge compilation, dynamic programming, and graph neural networks, as well as adapting these methods for specialized counting problems such as those involving pseudo-Boolean formulas or projected variables. These advancements are crucial for tackling increasingly complex problems in diverse fields, enhancing the efficiency and reliability of applications that rely on accurate model counts. The development of more efficient and robust model counters continues to be a significant area of active research.