Blocking and Other Enhancements for Bottom-Up Model Generation Methods

Abstract

Model generation is a problem complementary to theorem proving and is important for fault analysis and debugging of formal specifications of security protocols, programs and terminological definitions, for example. This paper discusses several ways of enhancing the paradigm of bottom-up model generation, with the two main contributions being a new range-restriction transformation and generalized blocking techniques. The range-restriction transformation refines existing transformations to range-restricted clauses by carefully limiting the creation of domain terms. The blocking techniques are based on simple transformations of the input set together with standard equality reasoning and redundancy elimination techniques, and allow for finding small, finite models. All possible combinations of the introduced techniques and a classical range-restriction technique were tested on the clausal problems of the TPTP Version 6.0.0 with an implementation based on the SPASS theorem prover using a hyperresolution-like refinement. Unrestricted domain blocking gave best results for satisfiable problems, showing that it is an indispensable technique for bottom-up model generation methods, that yields good results in combination with both new and classical range-restricting transformations. Limiting the creation of terms during the inference process by using the new range-restricting transformation has paid off, especially when using it together with a shifting transformation. The experimental results also show that classical range restriction with unrestricted blocking provides a useful complementary method. Overall, the results show bottom-up model generation methods are good for disproving theorems and generating models for satisfiable problems, but less efficient for unsatisfiable problems.