José Júlio Alferes

Dynamic updates of non-monotonic knowledge bases

In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads. We start by introducing the notion of an update P U of one logic program P by another logic program U. Subsequently, we provide a precise semantic characterization of P U , and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates P1 P2 ; defining a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, each describing a diAerent state of our knowledge of the world. DiAerent states may represent diAerent time points or diAerent sets of priorities or perhaps even diAerent viewpoints. Consequently, program modules may contain mutually contradictory as well as overlapping information. The role of the dynamic program update is to employ the mutual relationships existing between diAerent modules to precisely determine, at any given module composition stage, the declarative as well as the procedural semantics of the combined program resulting from the modules. ” 2000 Elsevier Science Inc. All rights reserved.

Evolving Logic Programs

Logic programming has often been considered less than adequate for modelling the dynamics of knowledge changing over time. In this paper we describe a simple though quite powerful approach to modelling the updates of knowledge bases expressed by generalized logic programs, by means of a new language, hereby christened EVOLP (after EVOlving Logic Programs). The approach was first sparked by a critical analysis of previous efforts and results in this direction [1,2,7,11], and aims to provide a simpler, and at once more general, formulation of logic program updating, which runs closer to traditional logic programming (LP) doctrine. From the syntactical point of view, evolving programs are just generalized logic programs (i.e. normal LPs plus default negation also in rule heads), extended with (possibly nested) assertions, whether in heads or bodies of rules. From the semantics viewpoint, a model-theoretic characterization is offered of the possible evolutions of such programs. These evolutions arise both from self (or internal) updating, and from external updating too, originating in the environment. This formulation sets evolving programs on a firm basis in which to express, implement, and reason about dynamic knowledge bases, and opens up a number of interesting research topics that we brush on.

Local closed world reasoning with description logics under the well-founded semantics

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.

The Refined Extension Principle for Semantics of Dynamic Logic Programming

Over recent years, various semantics have been proposed for dealing with updates in the setting of logic programs. The availability of different semantics naturally raises the question of which are most adequate to model updates. A systematic approach to face this question is to identify general principles against which such semantics could be evaluated. In this paper we motivate and introduce a new such principle the refined extension principle. Such principle is complied with by the stable model semantics for (single) logic programs. It turns out that none of the existing semantics for logic program updates, even though generalisations of the stable model semantics, comply with this principle. For this reason, we define a refinement of the dynamic stable model semantics for Dynamic Logic Programs that complies with the principle.

Abduction in well-founded semantics and generalized stable models via tabled dual programs

Abductive logic programming offers a formalism to declaratively express and solve problems in areas such as diagnosis, planning, belief revision and hypothetical reasoning. Tabled logic programming offers a computational mechanism that provides a level of declarativity superior to that of Prolog, and which has supported successful applications in fields such as parsing, program analysis, and model checking. In this paper we show how to use tabled logic programming to evaluate queries to abductive frameworks with integrity constraints when these frameworks contain both default and explicit negation. The result is the ability to compute abduction over well-founded semantics with explicit negation and answer sets. Our approach consists of a transformation and an evaluation method. The transformation adjoins to each objective literal

A Coherent Well-founded Model for Hybrid MKNF Knowledge Bases

O

Updates plus Preferences

in a program, an objective literal

Active rules in the semantic web: dealing with language heterogeneity

\hbox{\it not}(O)

Non-monotonic reasoning with logic programming

along with rules that ensure that

Multi-dimensional Dynamic Knowledge Representation

\hbox{\it not}(O)