Databases and related information systems can benefit from the use of ontologies to enrich the data with general background knowledge. The DL-Lite family of ontology languages was specifically tailored towards such ontology-based data access, enabling an implementation in a relational database management system (RDBMS) based on a query rewriting approach. In this paper, we propose an alternative approach to implementing ontology-based data access in DL-Lite. The distinguishing feature of our approach is to allow rewriting of both the query and the data. We show that, in contrast to the existing approaches, no exponential blowup is produced by the rewritings. Based on experiments with a number of real-world ontologies, we demonstrate that query execution in the proposed approach is often more efficient than in existing approaches, especially for large ontologies. We also show how to seamlessly integrate the data rewriting step of our approach into an RDBMS using views (which solves the update problem) and make an interesting observation regarding the succinctness of queries in the original query rewriting approach.

Ontologies can be used to provide an enriched vocabulary for the formulation of queries over instance data. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all data sets formulated in a given signature. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.

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.

In this paper, I defend a multiplicative approach that distinguishes statues from amounts of matter, political entities from physical ones, qua entities (e.g. John qua Alitalia passenger) from players (e.g. John), etc. I develop a theory of levels which is based on the primitive notions of level, parthood, and grounding (a kind of existential dependence) and that is used to characterize more specific relations like constitution, inherence, and abstraction. I neither aim to propose a `definitive' theory of levels nor to commit to their ontological or conceptual nature. Hence, the adjective `ontological' used in the title does not qualify the nature of the entities that belong to levels but the way the notion of level is characterized, i.e. in terms of general and philosophically well-founded notions. By keeping away from a purely realist attitude, I can then discuss the adequacy of some alternative first-order theories to account for three puzzling scenarios.

Existing action calculi provide rich, declarative formalisms for reasoning about actions. BDI-based programming languages like AgentSpeak, on the other hand, are procedural and geared towards practical applications of cognitive agents. In this paper, we close the gap between these two lines of research by integrating action calculi and AgentSpeak programs. Specifically, we develop a new and purely declarative semantics for AgentSpeak, which paves the way for combining this language with any suitable action calculus in a strictly modular fashion. As the main technical result, we prove that the new declarative semantics is correct wrt. the standard operational semantics for AgentSpeak. This provides the basis for a modular integration of a BDI-based agent programming language with sophisticated methods for reasoning about actions.

In this paper we introduce dialectical frameworks, a powerful generalization of Dung-style argumentation frameworks where each node comes with an associated acceptance condition. This allows us to model different types of dependencies, e.g. support and attack, as well as different types of nodes within a single framework. We show that Dung's standard semantics can be generalized to dialectical frameworks, in case of stable and preferred semantics to a slightly restricted class which we call bipolar frameworks. We show how acceptance conditions can be conveniently represented using weights respectively priorities on the links and demonstrate how some of the legal proof standards can be modeled based on this idea.

Dynamic epistemic logic deals with the representation of situations in a multi-agent and dynamic setting. It allows to express in a uniform way statements about: 1. what is true about an initial situation 2. what is true about an event occurring in this situation 3. what is true about the resulting situation after the event has occurred. We axiomatize in this framework what we can infer about (3) given (1) and (2), introducing thereby new techniques to prove completeness. We also show that this axiomatization is decidable. Besides being useful for reasoning about actions, it provides a natural characterization of the product update of dynamic epistemic logic.

A lot of mathematical knowledge has been formalized and stored in repositories by now: different mathematical theorems and theories have been taken into consideration and included in mathematical repositories. Applications more distant from pure mathematics, however --- though based on these theories --- often need more detailed knowledge about the underlying theories. In this paper we present an example Mizar formalization from the area of electrical engineering focusing on stability theory which is based on complex analysis. We discuss what kind of special knowledge is necessary here and which amount of this knowledge is included in existing repositories.

Luhmann (1984) defined society as a communication system which is structurally coupled to, but not an aggregate of, human action systems. The communication system is then considered as self-organizing ("autopoietic"), as are human actors. Communication systems can be studied by using Shannon's (1948) mathematical theory of communication. The update of a network by action at one of the local nodes is then a well-known problem in artificial intelligence (Pearl 1988). By combining these various theories, a general algorithm for probabilistic structure/action contingency can be derived. The consequences of this contingency for each system, its consequences for their further histories, and the stabilization on each side by counterbalancing mechanisms are discussed, in both mathematical and theoretical terms. An empirical example is elaborated.

Forward State Planning with Reachability Heuristics is arguably the most successful approach to Automated Planning up to date. In addition to an estimation of the distance to the goal, relaxed plans obtained with such heuristics provide the search with useful information such as helpful actions and look-ahead states. However, this information is extracted only from the beginning of the relaxed plan. In this paper, we propose using information extracted from the last actions in the relaxed plan to generate intermediate goals backwards. This allows us to use information from previous computations of the heuristic and reduce the depth of the search tree.