  Model counting, a fundamental task in computer science, involves determining
the number of satisfying assignments to a Boolean formula, typically
represented in conjunctive normal form (CNF). While model counting for CNF
formulas has received extensive attention with a broad range of applications,
the study of model counting for Pseudo-Boolean (PB) formulas has been
relatively overlooked. Pseudo-Boolean formulas, being more succinct than
propositional Boolean formulas, offer greater flexibility in representing
real-world problems. Consequently, there is a crucial need to investigate
efficient techniques for model counting for PB formulas.
  In this work, we propose the first exact Pseudo-Boolean model counter,
PBCount, that relies on knowledge compilation approach via algebraic decision
diagrams. Our extensive empirical evaluation shows that PBCount can compute
counts for 1513 instances while the current state-of-the-art approach could
only handle 1013 instances. Our work opens up several avenues for future work
in the context of model counting for PB formulas, such as the development of
preprocessing techniques and exploration of approaches other than knowledge
compilation.
