ABSTRACT. (Co)-authoring, (re)using, and maintaining complex artifacts such as knowledge bases, ontologies, or software are complex tasks that are supported by a range of powerful tools and for which best practice/methodologies have been developed. Some of these relate to \emph{modularity} and \emph{patterns}: the former has been investigated for ontology engineering with some interesting results related to re-use and also to automated reasoning and optimisation. (Design) patterns have been hailed as a reusable engineering solution that supports high quality design, reuse, and comprehension in a number of disciplines including software and ontology engineering. For ontologies, a lot of effort has been spent on identifying, collecting, and classifying patterns - with seemingly little effect on ontology engineering practice. SNOMED CT is an interesting case in point as it is increasingly built using modelling templates as well as tools designed to support the usage of these templates, but the resulting OWL ontology has no signs of the usage of these templates. Recently, we developed a generic framework for identifying \emph{regularities} in formal languages and investigated various variants of the problem of finding minimal rewritings of a given language using \emph{macros}. We have developed related algorithms and applied these to existing, large OWL ontologies including SNOMET C; we found that we can find minimal rewritings of these in less than a minute. An interesting point of this application relates to the rather unusual syntax of OWL that poses some non-trivial problems due to the presence of a mixture of ranked and unranked symbols such as “SubClassOf” being binary whereas “DisjointClasses” taking basically any number of symbols.

In this talk, I will provide a general overview of the (ontology) engineering challenges mentioned above around modularity and patterns/regularities, and discuss various related solutions and insights gained.

ABSTRACT. The growing interest in extensions of Dung’s abstract argumentation frameworks has recently led to the simultaneous and independent discovery of a combination of two of these extensions: Bipolar Argumentation Frameworks (BAFs), where a relation representing supports between arguments is added, and Incomplete Argumentation Frameworks (IAFs), where the existence of arguments and attacks may be uncertain. This paper digs deeper into such a combination by: (i) providing a thoughtful analysis of the existing notions of completion (the hypothetical removal of uncertainty used in IBAFs to reason about argument acceptability); (ii) proposing, motivating and studying a new notion of completion; (iii) throwing new complexity results on argument acceptability problems associated with IBAFs; (iv) encoding these reasoning problems on a lightweight version of dynamic logic.

ABSTRACT. Structured argumentation formalisms provide a rich framework to formalise and reason over situations where contradicting information is present. However, in most formalisms the integral step of constructing all possible arguments is performed in an unconstrained way and is thus not under direct control of the user. This can hinder a solid analysis of the behaviour of the system and makes explanations for the results difficult to obtain. In this work, we introduce a general approach that allows constraining the derivation of arguments for assumption-based argumentation. We show that, under certain conditions, this reduces to eliminating rules from the given knowledge base while letting the derivation of arguments unconstrained. For this as well as for the general approach to derivation constraining, we provide an encoding into Answer Set Programming.

ABSTRACT. Model-based diagnosis is a technique for identifying malfunctioning components in systems. While it has successfully been applied to systems such as digital circuits, this paper aims to extend applicability to systems such as programs that process values from large domains, for example, term structures. In these cases, especially when multiple components may be faulty, it is challenging to identify a diagnosis that provides a consistent model with respect to the specified domain.This paper presents an Answer-Set Programming (ASP) based method for computing such diagnosis. We are particularly interested in functional circuits over domains of values, such as rational numbers and inductive data types, to diagnose faults in programming assignments in order to advance intelligent tutoring systems. This article shows how a consistent diagnosis, justified by intermediate values, can be achieved efficiently using ASP. Additionally, an adaption to Constraint Answer Set Programming with s(CASP) is presented that avoids grounding, allowing domain sizes to be handled that are too large to be grounded.

ABSTRACT. The maintenance optimization of multicomponent machines has been recently formalized as an Answer Set Optimization (ASO) problem based on component selection and grouping of overlapping maintenance intervals. The motivation of the current work is to develop an extension that would integrate resources and availability constraints into this maintenance model. This paper outlines an extended ASP model with the primary focus on modeling and optimizing costly maintenance resources, culminating in cost savings facilitated by the progressive development of workforce competence. The models presented in this work extend the cost function of the prior ASP formalization in a modular way with additional cost priorities concerning availability, parallelism, workforce, and expertise. Due to the presented extensions, the complexity of the integrated maintenance model increases compared to the prior formalization.