We present a method for generating possible proofs of a query with respect to a given Answer Set Programming (ASP) rule set using an abductive process where the space of abducibles is automatically constructed just from the input rules alone. Given a (possibly empty) set of user provided facts, our method infers any additional facts that may be needed for the entailment of a query and then outputs these extra facts, without the user needing to explicitly specify the space of all abducibles. We also present a method to generate a set of directed edges corresponding to the justification graph for the query. Furthermore, through different forms of implicit term substitution, our method can take user provided facts into account and suitably modify the abductive solutions. Past work on abduction has been primarily based on goal directed methods. However these methods can result in solvers that are not truly declarative. Much less work has been done on realizing abduction in a bottom up solver like the Clingo ASP solver. We describe novel ASP programs which can be run directly in Clingo to yield the abductive solutions and directed edge sets without needing to modify the underlying solving engine.

We consider the problem of verifying graph transformations described by an imperative programming language. This question is particularly relevant for transformation of knowledge bases. We will argue in this paper that previous proof approaches based on dedicated Description Logics were technically complex and often inappropriate for reasoning about preservation of structure of knowledge bases. For these reasons, we explore here an assertion formalism based on the Guarded Fragment of predicate logic, which provides a homogeneous framework. Based on a formal semantics of our transformation language, we show how to extract proof obligations from annotated programs and how to obtain decidable correctness problems.

This article explores methods to provide computer support for reasoning about graph transformations. We first define a general framework for representing graphs, graph morphisms and single graph rewriting steps. This setup allows for interactively reasoning about graph transformations. In order to achieve a higher degree of automation, we identify fragments of the graph description language in which we can reduce reasoning about global graph properties to reasoning about local properties, involving only a bounded number of nodes, which can be decided by Boolean satisfiability solving or even by deterministic computation of low complexity.