Abstract
An efficient method for minimal model generation is presented. The method employs branching assumptions and lemmas so as to prune branches that lead to nonminimal models, and to reduce minimality tests on obtained models. This method is applicable to other approaches such as Bry’s complement splitting and constrained search or Niemelä’s groundedness test, and greatly improves their efficiency. We implemented MM-MGTP based on the method. Experimental results with MM-MGTP show a remarkable speedup compared to MM-SATCHMO.
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Hasegawa, R., Fujita, H., Koshimura, M. (2000). Efficient Minimal Model Generation Using Branching Lemmas. In: McAllester, D. (eds) Automated Deduction - CADE-17. CADE 2000. Lecture Notes in Computer Science(), vol 1831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10721959_15
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DOI: https://doi.org/10.1007/10721959_15
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