This paper introduces an abductive framework for updating knowledge bases represented by extended disjunctive programs. We first provide a simple transformation from abductive programs to update programs which are logic programs specifying changes on abductive hypotheses. Then, extended abduction, which was introduced by the same authors as a generalization of traditional abduction, is computed by the answer sets of update programs. Next, different types of updates, view updates and theory updates are characterized by abductive programs and computed by update programs. The task of consistency restoration is also realized as special cases of these updates. Each update problem is comparatively assessed from the computational complexity viewpoint. The result of this paper provides a uniform framework for different types of knowledge base updates, and each update is computed using existing procedures of logic programming.
Buccafurri @math introduce an inheritance program which consists of a set of EDPs ordered by a generality relation. It realizes default reasoning in inheritance hierarchies and is also applied to updating logic programs. According to @cite_23 , inheritance programs are equivalent to update programs of Eiter @math 's, hence the same arguments as the comparison with update programs are applied.
@cite_23
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This paper introduces techniques for updating knowledge bases represented in extended logic programs. Three different types of updates, view updates, theory updates, and inconsistency removal, are considered. We formulate these updates through abduction, and provide methods for computing them with update programs. An update program is an extended logic program which specifies changes on abductive hypotheses, then updates are computed by the U-minimal answer sets of an update program. The proposed technique provides a uniform framework for these different types of updates, and each update is computed using existing procedures of logic programming. Among others, (1998) presented an approach for updating logic programs with sets of rules based on dynamic logic programs. We syntactically redefine dynamic logic programs and investigate their semantical properties, looking at them from perspectives such as a belief revision and abstract consequence relation view. Since the approach does not respect minimality of change, we refine its stable model semantics and present minimal stable models and strict stable models. We also compare the update approach to related work, and find that is equivalent to a class of inheritance programs independently defined by (1999). We present FJig, a simple calculus where basic building blocks are classes in the style of Featherweight Java, declaring fields, methods and one constructor. However, inheritance has been generalized to the much more flexible notion originally proposed in Bracha's Jigsaw framework. That is, classes play also the role of modules, that can be composed by a rich set of operators, all of which can be expressed by a minimal core. We keep the nominal approach of Java-like languages, that is, types are class names. However, a class is not necessarily a structural subtype of any class used in its defining expression. The calculus allows the encoding of a large variety of different mechanisms for software composition in class-based languages, including standard inheritance, mixin classes, traits and hiding. Hence, FJig can be used as a unifying framework for analyzing existing mechanisms and proposing new extensions. We provide two different semantics of an FJig program: flattening and direct semantics. The difference is analogous to that between two intuitive models to understand inheritance: the former where inherited methods are copied into heir classes, and the latter where member lookup is performed by ascending the inheritance chain. Here we address equivalence of these two views for a more sophisticated composition mechanism. The paper proposes a new knowledge representation language, called DLP<, which extends disjunctive logic programming (with strong negation) by inheritance. The addition of inheritance enhances the knowledge modeling features of the language providing a natural representation of default reasoning with exceptions. A declarative model-theoretic semantics of DLP< is provided, which is shown to generalize the Answer Set Semantics of disjunctive logic programs. The knowledge modeling features of the language are illustrated by encoding classical nonmonotonic problems in DLP<. The complexity of DLP< is analyzed, proving that inheritance does not cause any computational overhead, as reasoning in DLP< has exactly the same complexity as reasoning in disjunctive logic programming. This is confirmed by the existence of an efficient translation from DLP< to plain disjunctive logic programming. Using this translation, an advanced KR system supporting the DLP< language has been implemented on top of the DLV system and has subsequently been integrated into DLV.