State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Reasoning about evolving nonmonotonic knowledge bases

Recently, several approaches to updating knowledge bases modeled as extended logic programs have been introduced, ranging from basic methods to incorporate (sequences of) sets of rules into a logic program, to more elaborate methods which use an update policy for specifying how updates must be incorporated. In this paper, we introduce a framework for reasoning about evolving knowledge bases, which are represented as extended logic programs and maintained by an update policy. We first describe a formal model which captures various update approaches, and we define a logical language for expressing properties of evolving knowledge bases. We then investigate semantical and computational properties of our framework, where we focus on properties of knowledge states with respect to the canonical reasoning task of whether a given formula holds in a given evolving knowledge base. In particular, we present finitary characterizations of the evolution for certain classes of framework instances, which can be exploited for obtaining decidability results. In more detail, we characterize the complexity of reasoning for some meaningful classes of evolving knowledge bases, ranging from polynomial to double exponential space complexity