Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. The inexpressive Description Logic EL is of particular interest in this context since, on the one hand, several large biomedical ontologies are defined using EL. On the other hand, unification in EL has recently been shown to be NP-complete, and thus of significantly lower complexity than unification in other DLs of similarly restricted expressive power. However, the unification algorithms for EL developed so far cannot deal with general concept inclusion axioms (GCIs). This paper makes a considerable step towards addressing this problem, but the GCIs our new unification algorithm can deal with still need to satisfy a certain cycle restriction.

In this paper, we study the problem of exchanging knowledge between a source and a target knowledge base (KB), connected through mappings. Differently from the traditional database exchange setting, which considers only the exchange of data, we are interested in exchanging implicit knowledge. As representation formalism we use Description Logics (DLs), thus assuming that the source and target KBs are given as a DL TBox+ABox, while the mappings have the form of DL TBox assertions. We study the problem of translating the knowledge in the source KB according to these mappings. We define a general framework of KB exchange, and address the problems of representing implicit source information in the target, and of computing different kinds of solutions, i.e., target KBs with specified properties, given a source KB and a mapping. We develop first results and study the complexity of KB exchange for DL-Lite_RDFS, a DL corresponding to the FOL fragment of RDFS, and for DL-Lite_R.

In this paper, we present an alternative to the Turing Test that has some conceptual and practical advantages. A Winograd schema is a pair of sentences that differ only in one or two words and that contain a referential ambiguity that is resolved in opposite directions in the two sentences. We have compiled a collection of Winograd schemas, designed so that the correct answer is obvious to the human reader, but cannot easily be found using selectional restrictions or statistical techniques over text corpora. A contestant in the Winograd Schema Challenge is presented with a collection of one sentence from each pair, and required to achieve human-level accuracy in choosing the correct disambiguation.

Answer Set Programming (ASP) is a fully-declarative logic programming paradigm, which has been proposed in the area of knowledge representation and non-monotonic reasoning. Nowadays, the formal properties of ASP are well-understood, efficient ASP systems are available, and, recently, ASP has been employed in a few industrial applications. However, ASP technology is not mature for a successful exploitation in industry yet; mainly because ASP technologies are not integrated in the well-assessed development processes and platforms which are tailored for imperative/object-oriented programming languages. In this paper we present a new programming framework blending ASP with Java. The framework is based on JASP, an hybrid language that transparently supports a bilateral interaction between ASP and Java. JASP specifications are compliant with the JPA standard to perfectly fit extensively-adopted enterprise application technologies. The framework also encompasses an implementation of JASP as a plug-in for the Eclipse platform, called JDLV, which includes a compiler from JASP to Java. Moreover, we show a real-world application developed with JASP and JDLV, which highlights the effectiveness of our ASP–Java integration framework.

A bipolar framework is introduced for combining agents' beliefs so as to enable them to reach a common shared position or viewpoint. Our approach exploits the truth-gaps inherent to propositions involving vague concepts, by allowing agents to soften directly conflicting opinions. To this end we adopt a bipolar truth-model for propositional logic characterised by lower and upper valuations on the sentences of the language. According to this model sentences may be absolutely true, absolutely false or borderline (i.e. neither absolutely true nor absolutely false). The added flexibility of a possible truth-gap between absolutely true and absolutely false allows agents with inconsistent viewpoints, in which a proposition p is absolutely true according to one view and absolutely false according to the other, to reach a compromise position in which p is borderline. Within this framework four combination operators are proposed for combining different viewpoints as represented by different valuation pairs. Intuitively, these correspond to compromise positions with different levels of semantic precision (or vagueness). Kleene belief pairs are then introduced as lower and upper measures quantifying epistemic uncertainty about the sentences of the language when valuation pairs provide the underlying truth model. The combination operators on valuation pairs are then extended to belief pairs using a general schema incorporating a probabilistic model of the interaction between agents. The properties of the four operators are then investigated within this extended framework.

Possibilistic logic is a well-known logic for reasoning under uncertainty, which is based on the idea that the epistemic state of an agent can be modeled by assigning to each possible world a degree of possibility, taken from a totally ordered, but essentially qualitative scale. Recently, a generalization has been proposed that extends possibilistic logic to a meta-epistemic logic, endowing it with the capability of reasoning about epistemic states, rather than merely constraining them. In this paper, we further develop this generalized possibilistic logic (GPL). We introduce an axiomatization showing that GPL is a fragment of a graded version of the modal logic KD, and we prove soundness and completeness w.r.t. a semantics in terms of possibility distributions. Next, we reveal a close link between the well-known stable model semantics for logic programming and the notion of minimally specific models in GPL. More generally, we analyze the relationship between the equilibrium logic of Pearce and GPL, showing that GPL can essentially be seen as a generalization of equilibrium logic, although its notion of minimal specificity is slightly more demanding than the notion of minimality underlying equilibrium logic.

A constraint satisfaction problem has compactness if any infinite set of constraints is satisfiable whenever all its finite subsets are satisfiable. We prove a sufficient condition for compactness, which holds for a range of problems including those based on the well-known Interval Algebra (IA) and RCC8. Furthermore, we show that compactness leads to a useful necessary and sufficient condition for the recently introduced patchwork property, namely that patchwork holds exactly when every satisfiable finite network (i.e., set of constraints) has a canonical solution, that is, a solution that can be extended to a solution for any satisfiable finite extension of the network. Applying these general theorems to qualitative reasoning, we obtain important new results as well as significant strengthenings of previous results regarding IA, RCC8, and their fragments and extensions. In particular, we show that all the maximal tractable fragments of IA and RCC8 (containing the base relations) have patchwork and canonical solutions as long as networks are algebraically closed.

We present a conflict-based approach to diagnosing Discrete Event Systems (DES) which generalises Reiter's Diagnose algorithm to a much broader class of problems. This approach obviates the need to explicitly reconstruct the system's behaviors that are consistent with the observation, as is typical of existing DES diagnosis algorithms. Instead, our algorithm explores the space of diagnosis hypotheses, testing hypotheses for consistency, and generating conflicts which rule out successors and other portions of the search space. Under relatively mild assumptions, our algorithm correctly computes the set of preferred diagnosis candidates. We investigate efficient symbolic representations of the hypotheses space and provide a SAT-based implementation of this framework which is used to address a real-world problem in processing alarms for a power transmission system.

We consider the problem of synthesizing an agent pro- tocol satisfying LTL specifications for multiple, partially- observable environments. We present a sound and complete procedure for solving the synthesis problem in this setting and show it is computationally optimal from a theoretical com- plexity standpoint. While this produces perfect-recall, hence unbounded, strategies we show how to transform these into agent protocols with bounded number of states.

Abstraction has been used in search and planning from the very beginning of AI. Many different methods and formalisms for abstraction have been proposed in the literature but they have been designed from various points of view and with varying purposes. Hence, these methods have been notoriously difficult to analyse and compare in a structured way. In order to improve upon this situation, we present a coherent and flexible framework for modelling abstraction (and abstraction-like) methods based on transformations on labelled graphs. Transformations can have certain method properties that are inherent in the abstraction methods and describe their fundamental modelling characteristics, and they can have certain instance properties that describe algorithmic and computational characteristics of problem instances. The usefulness of the framework is demonstrated by applying it to problems in both search and planning. First, we show that we can capture many search abstraction concepts (such as avoidance of backtracking between levels) and that we can put them into a broader context. We further model five different abstraction concepts from the planning literature. Analysing what method properties they have highlights their fundamental differences and similarities. Finally, we prove that method properties sometimes imply instance properties. Taking also those instance properties into account reveals important information about computational aspects of the five methods.