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In this work, a new approach for creating Pattern Databases (PDBs) is suggested that induces non-consistent heuristic functions just by recognizing feasible (yet admissible) heuristic values. This approach serves to generalize even further the BPMX propagation rule, that will work now even in directed graphs. Experiments in different state spaces show a noticeable improvement over the Scalar Pattern Databases.
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