Horn fragments of Description Logics (DLs) have gained popularity because they provide a beneficial trade-off between expressive power and computational complexity and, more specifically, are usually tractable w.r.t. data complexity. Despite their potential, and partly due to the intricate interaction of nominals (O), inverses (I) and counting (Q), such fragments had not been studied so far for the DLs SHOIQ and SROIQ that underly OWL 1 and 2. In this paper, we present a polynomial and modular translation from Horn-SHOIQ knowledge bases into DATALOG, which shows that standard reasoning tasks are feasible in deterministic single exponential time. This improves over the previously known upper bounds, and contrasts the known NEXPTIME completeness of full SHOIQ. Thereby, Horn-SHOIQ stands out as the first EXPTIME complete DL that allows simultaneously for O, I, and Q. In addition, we show that standard reasoning in Horn-SROIQ is 2-EXPTIME complete. Despite their high expressiveness, both Horn-SHOIQ and Horn-SROIQ have polynomial data complexity. This makes them particularly attractive for reasoning in semantically enriched systems with large data sets. A promising first step in this direction could be achieved exploiting existing DATALOG engines, along the lines of our translation.

Description Logics (DLs) are knowledge representation formalisms that provide, for example, the logical underpinning of the W3C OWL standards. Conjunctive queries (CQs), the standard query language in databases, have recently gained significant attention for querying DL knowledge bases. Several different techniques are available for a wide range of DLs. Nevertheless, for OWL 1 DL and OWL 2 DL, decidability of CQ entailment is an open problem. So far, the combination of nominals, inverse roles, and number restrictions caused unsolvable problems. We tackle this problem and present a decidability result for entailment of unions of CQs in a DL with all three problematic constructors. For queries with only simple roles, our result also shows decidability in the logic that underpins OWL 1 DL and we believe that the presented results will pave the way for further progress towards CQ entailment decision procedures for OWL.

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.

Abstract argumentation frameworks have received a lot of interest in recent years. Most computational problems in this area are intractable but several tractable fragments have been identified. In particular, Dunne showed that many problems can be solved in linear time for argumentation frameworks of bounded tree-width. However, these tractability results, which were obtained via Courcelle’s Theorem, do not directly lead to efficient algorithms. The goal of this paper is to turn the theoretical tractability results into efficient algorithms and to explore the potential of directed notions of tree-width for defining larger tractable fragments.

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.

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.

We present a distributed algorithm for computing equilibria of heterogeneous nonmonotonic multi-context systems (MCS). The algorithm can be parametrized to compute only partial equilibria, which can be used for reasoning tasks like query answering or satisfiability checking that need only partial information and not whole belief states. Furthermore, caching is employed to cut redundant solver calls. As a showcase, we instantiate the MCS framework with answer set program contexts. To characterize equilibria of such MCS, we develop notions of loop formulas that enable reductions to the classical satisfiability problem (SAT). Notably, loop formulas for bridge rules between contexts and for the local contexts can be combined to a uniform encoding of an MCS into a (distributed) SAT instance. As a consequence, we can use SAT solvers for belief set building. We demonstrate this approach by an experimental prototype implementation, which uses an off-the-shelf SAT solver.

Abduction is a fundamental and important form of non-monotonic reasoning. Given a knowledge base explaining how the world behaves it aims at finding an explanation for some observed manifestation. In this paper we focus on propositional abduction, where the knowledge base and the manifestation are represented by propositional formulae. The problem of deciding whether there exists an explanation has been shown to be Σ p 2 -complete in general. We consider variants obtained by restricting the allowed connectives in the formulae to certain sets of Boolean functions. We give a complete classification of the complexity for all considerable sets of Boolean functions. In this way, we identify easier cases, namely NP-complete and polynomial cases; and we highlight sources of intractability. Further, we address the problem of counting the explanations and draw a complete picture for the counting complexity.

We often deal with dynamic worlds where actions are executed by agents and events may happen. Example of such worlds range from virtual worlds such as the world of a database to robots and humans in physical worlds. To understand the dynamics of such worlds as well as to be able to assert some control over such worlds one needs to reason about the actions and events and how they may change the world. In this invited talk we will present some of the important results in this field and present some future directions. In particular, we will discuss how theories and results from reasoning about actions and change can be combined with theories and results in dynamic epistemic logics to obtain a unified theory of multi-agent actions.

This paper constructively proves the existence of an effective procedure generating a computable (total) function that is not contained in any given effectively enumerable set of such functions. The proof implies the existence of machines that process informal concepts such as computable (total) functions beyond the limits of any given Turing machine or formal system, that is, these machines can, in a certain sense, "compute" function values beyond these limits. We call these machines creative. We argue that any "intelligent" machine should be capable of processing informal concepts such as computable (total) functions, that is, it should be creative. Finally, we introduce hypotheses on creative machines which were developed on the basis of theoretical investigations and experiments with computer programs. The hypotheses say that machine intelligence is the execution of a self-developing procedure starting from any universal programming language and any input.