Constrained decision diagrams

Abstract

A general n-ary constraint is usually represented explicitly as a set of its solution tuples, which may need exponential space. In this paper, we introduce a new representation for general n-ary constraints called Constrained Decision Diagram (CDD). CDD generalizes BDD-style representations and the main feature is that it combines constraint reasoning/consistency techniques with a compact data structure. We present an application of CDD for recording all solutions of a conjunction of constraints. Instead of an explicit representation, we can implicitly encode the solutions by means of constraint propagation. Our experiments confirm the scalability and demonstrate that CDDs can drastically reduce the space needed over explicit and ZBDD representations. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.