Approximate Model Counting

Approximate model counting aims to efficiently estimate the number of solutions to a Boolean formula, a computationally hard problem with broad applications. Recent research emphasizes developing faster and more reliable algorithms, such as those based on hashing and knowledge compilation, often incorporating techniques from SAT solvers and focusing on providing verifiable accuracy guarantees (e.g., through certificates). This active area of research is driven by the need for scalable and trustworthy approximate solutions in diverse fields like artificial intelligence and formal verification, where exact model counting is often intractable.