Splitting Techniques for Conditional Belief Bases in the Context of c-Representations

Marco Wilhelm, Meliha Sezgin, Gabriele Kern-Isberner, Jonas Haldimann, Christoph Beierle, Jesse Heyninck

Abstract: Splitting belief bases is fundamental for efficient reasoning and for better understanding interrelationships among the knowledge entities. In this paper, we survey the most important splitting techniques for conditional belief bases in the context of c-representations which constitute a specific class of ranking models with outstanding behavior not only with respect to belief base splitting, as shown in recent papers. We provide a splitting hierarchy, in particular by proving that safe conditional syntax splittings and case splittings are so-called CSP-constraint splittings. We advance the level of knowledge about CSP-constraint splittings and present an algorithm for computing CSP-constraint splittings.