In previous work, we proposed a "saturation" algorithm for symbolic state-space generation characterized by the use of multi-valued decision diagrams, boolean Kronecker operators, event locality, and a special iteration strategy. This approach outperforms traditional BDD-based techniques by several orders of magnitude in both space and time but, like them, assumes a priori knowledge of each submodel's state space. We introduce a new algorithm that merges explicit local state-space discovery with symbolic global state-space generation. This relieves the modeler from worrying about the behavior of submodels in isolation. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Ciardo, G., Marmorstein, R., & Siminiceanu, R. (2003). Saturation unbound. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2619, 379–393. https://doi.org/10.1007/3-540-36577-x_27
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