Abstract. We study the application of limited-width MDDs (multivalued decision diagrams) as discrete relaxations for combinatorial optimization problems. These relaxations are used...
David Bergman, Willem Jan van Hoeve, John N. Hooke...
Several variants of Bryant's ordered binary decision diagrams have been suggested in the literature to reason about discrete functions. In this paper, we introduce a generic ...
Dynamic programming algorithms provide a basic tool identifying optimal solutions in Markov Decision Processes (MDP). The paper develops a representation for decision diagrams sui...
The paper is an overview of a recently developed compilation data structure for graphical models, with specific application to constraint networks. The AND/OR Multi-Valued Decision...
Shared decision diagram representations of a state-space have been shown to provide efficient solutions for model-checking of large systems. However, decision diagram manipulation ...
Dynamic programming algorithms have been successfully applied to propositional stochastic planning problems by using compact representations, in particular algebraic decision diag...
Abstract. We show how case bases can be compiled into Decision Diagrams, which represent the cases with reduced redundancy. Numerous computations can be performed efficiently on th...
Abstract. Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Ordered Decision Diagrams with AND nodes, in order to capture function de...
Decision diagrams (DDs) have made their way into Petri net (PN) tools either in the form of new tools (usually designed from scratch to use DDs) or as enhancements to existing tool...
Junaid Babar, Marco Beccuti, Susanna Donatelli, An...
Multiple-valued decision diagrams (MDDs) give a way of approaching problems by using symbolic variables which are often more naturally associated with the problem statement than t...