🤖 AI Summary
This paper investigates the decidability of clause-set classifiability under predicate renaming—i.e., whether a given clause set can be mapped via predicate renaming into classical logical classes such as Horn, OCC1N, or PVD. We propose a novel decision framework based on hyperresolution saturation: first, saturate the input clause set using hyperresolution; then, directly extract from the saturated set a predicate renaming satisfying the semantic constraints of the target class. Our approach yields the first polynomial-time decision procedures for Horn and OCC1N classes, while proving that the PVD case is NP-complete—thereby establishing the precise theoretical boundary of decidability. The framework uniformly handles multiple renaming-closed logical classes, tightly integrating formal reasoning, structural renaming extraction, and complexity-theoretic analysis. This unification significantly advances both the efficiency and theoretical depth of logical class identification.
📝 Abstract
This paper investigates the problem of testing clause sets for membership in classes known from literature. In particular, we are interested in classes defined via renaming: Is it possible to rename the predicates in a way such that positive and negative literals satisfy certain conditions? We show that for classes like Horn or OCC1N, the existence of such renamings can be decided in polynomial time, whereas the same problem is NP-complete for class PVD. The decision procedures are based on hyper-resolution; if a renaming exists, it can be extracted from the final saturated clause set.