This paper proposes a layered graph model for representing the internal structure of complex plane regions, where each node represents the closure of a connected component of the interior or exterior of a complex region. The model provides a complete representation in the sense that the (global) nine-intersections between the interiors, the boundaries, and the exteriors of two complex regions can be determined by the (local) RCC8 relations between associated simple regions. 

Even in small ontologies that only contain Figure 1: A justification for Person tens of axioms, there can be multiple reasons for an entailment, none of which may be obvious. It is for this a number of justifications that all participants ranked "difficult" reason that there has recently been a lot of focus on generating to "impossible" to understand. This includes people explanations for entailments in ontologies. In the who have over two years experience of working with OWL, OWL world, justifications are a popular form of explanation building ontologies and even includes people who have developed for entailments. A justification is a minimal subset OWL reasoners. This is indicative that justification of an ontology that is sufficient for an entailment to hold understanding can be a real problem.

The applicability conditions of legal Norms regulating computer systems can be modelled in different rules very often refer to these institutional concepts, rather ways, see, for example, (Boella, van der Torre, and than to so called brute facts. To simplify the notation we refer Verhagen 2008). If norms are represented by hard constraints, to the former as constitutive rules, and the latter simply then computer systems are designed to avoid violations.

Ambient Intelligence environments consist of various devices that collect, process, change and share the available context information. The imperfect nature of context, the open and dynamic nature of ambient environments, and the special characteristics of the involved devices have introduced new research challenges in the field of KR. Previous work presented a solution based on an extension of multi-context systems through the use of defeasible reasoning to reason efficiently with conflicts. This paper reports on initial experiences gained from the deployment of contextual defeasible reasoning in real environments. We report on the architecture of an implementation on small devices, present the definition and implementation of two concrete application scenarios, and discuss the performance and issues of scalability of the approach.

By logical proportion, we mean a statement that expresses a semantical equivalence between two pairs of propositions. In these pairs, each element is compared to the other in terms of similarities and/or dissimilarities. An example of such a proportion is the well known analogical proportion: a is to b as c is to d . Analogical proportions have been recently characterized in logical terms, but there are many other proportions that are worth of interest. Some of them can be related to the analogical pattern, others are related to semantical equivalence between conditional objects and express statements such as a ressembles to b and differs from b in the same way as c with respect to d. We show that there are 5 direct proportions, including the analogical one and 4 others having a conditional object flavor, where the change (if any) from a to b goes in the same direction as the change from c to d (if any), together with 5 reverse proportions obtained by switching c and d. Moreover, there exists only one auto-reverse proportion called paralogy and stating that what a and b have in common, c and d have it as well. It is then established that there is none other proportion than these ones (with the exception of 4 degenerated ones) that satisfies a natural “full identity” requirement. The paper proposes a structured and unified view of these logical proportions and discusses their characteristic properties. It extends previous works where only proportions related to analogy were considered. It also explores the use of these logical proportions in transduction-like inference, where new items are classified on the basis of already classified items without trying to induce a generic model, considering similarities and differences between items only. Taking advantage of different proportions, a transduction procedure is proposed.

In this paper, we consider propositional Topological logics are a family of languages for representing topological logics with connectedness, i.e. topological and reasoning about topological data. The non-logical logics in which the only logical connectives are the usual primitives of these languages stand for various topological Boolean operators, but where there is a non-logical primitive relations and operations, and their valid formulas encode our expressing the property of topological connectedness knowledge about those relations and operations. Consider, (or a variant thereof). We show that such topological logics for example, the six relations illustrated in Figure 1. By em-are typically sensitive both to the spaces they are interpreted over and--more particularly--to the subsets of those spaces over which their variables are allowed to range.

We study logical principles connecting two relations: independence, which is known as nondeducibility in the study of information flow, and functional dependence. Two different epistemic interpretations for these relations are discussed: semantics of secrets and probabilistic semantics. A logical system sound and complete with respect to both of these semantics is introduced and is shown to be decidable.

Cardinality constraints or, more generally, weight constraints are well recognized as an important extension of answer-set programming. Clearly, all common algorithmic tasks related to programs with cardinality or weight constraints (PWCs) - like checking the consistency of a program - are intractable. Many intractable problems in the area of knowledge representation and reasoning have been shown to become tractable if the treewidth of the programs or formulas under consideration is bounded by some constant. The goal of this paper is to apply the notion of treewidth to PWCs and to identify tractable fragments. It will turn out that the straightforward application of treewidth to PWCs does not suffice to obtain tractability. However, by imposing further restrictions, tractability can be achieved.

We address the problem of repairing large-scale biological networks and corresponding yet often discrepant measurements in order to predict unobserved variations. To this end, we propose a range of different operations for altering experimental data and/or a biological network in order to re-establish their mutual consistency-an indispensable prerequisite for automated prediction. For accomplishing repair and prediction, we take advantage of the distinguished modeling and reasoning capacities of Answer Set Programming. We validate our framework by an empirical study on the widely investigated organism Escherichia coli.

We study the problem of reasoning from incoherent answer set programs, i.e., from logic programs that do not have an answer set due to cyclic dependencies of an atom from its default negation. As a starting point we consider so-called semi-stable models which have been developed for this purpose building on a program transformation, called epistemic transformation. We give a model-theoretic characterization of this semantics, considering pairs of two-valued interpretations of the original program, rather than resorting to its epistemic transformation. Moreover, we show some anomalies of semi-stable semantics with respect to basic epistemic properties and propose an alternative semantics satisfying these properties. In addition to a model-theoretic and a transformational characterization of the alternative semantics, we prove precise complexity results for main reasoning tasks under both semantics.