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.

We present a formal semantical model to capture action, belief and intention, based on the "database perspective" (Shoham, 2009). We then provide postulates for belief and intention revision, and state a representation theorem relating our postulates to the formal model. Our belief postulates are in the spirit of the AGM theory; the intention postulates stand in rough correspondence with the belief postulates.

While type causality helps us to understand general relationships such as the etiology of a disease (smoking causing lung cancer), token causality aims to explain causal connections in specific instantiated events, such as the diagnosis of a patient (Ravi’s developing lung cancer after a 20-year smoking habit). Understanding why something happened, as in these examples, is central to reasoning in such diverse cases as the diagnosis of patients, understanding why the US financial market collapsed in 2007 and finding a causal explanation for Obama’s victory over Clinton in the US primary. However, despite centuries of work in philosophy and decades of research in computer science, the problem of how to rigorously formalize token causality and how to automate such reasoning has remained unsolved. In this paper, we show how to use type-level causal relationships, represented as temporal logic formulas, together with philosophical principles, to reason about these token-level cases.

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.

Reasoning with dates and duration has long been addressed by the community. Existing duration ontologies, however, lack complete axiomatizations of their intended models; many simply represent timedurations as real numbers and treat the duration function as a metric on the timeline. We show that such approaches are inadequate and provide a first-order ontology of duration that overcomes these limitations.

In the field of psychology, a considerable amount of knowledge is expressed using only natural language, which complicates accurate studies and comparisons. We believe that Answer Set Programming (ASP) can be used successfully for the formalization of psychological knowledge. To demonstrate the viability of ASP for this task, in this paper we develop an ASP-based formalization of the mechanics of Short-Term Memory, and show how it correctly reproduces the observed behavior of human subjects.

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.