Decision Diagram

Decision diagrams (DDs), including Binary Decision Diagrams (BDDs) and their variants like Zero-suppressed DDs (ZDDs) and Multi-valued DDs (MVDDs), are graph-based data structures used to efficiently represent Boolean functions and solve combinatorial optimization problems. Current research focuses on improving DD efficiency through techniques like sparsification (e.g., using machine learning to reduce diagram size), optimizing variable ordering for faster computation, and extending DDs to handle more complex logical frameworks such as pseudo-Boolean formulas and multi-objective optimization. These advancements enhance the applicability of DDs in diverse fields, including formal verification, model counting, multi-criteria decision making, and even machine learning model interpretation and optimization.