Publications

A recent literature review indicated that the settings-based approach, as it is applied to sports clubs, is poorly understood and not well implemented. The current study builds on the health promoting sports clubs (HPSC) theoretical model as a basis for a settings-based approach in the sports setting and proposes a logic model to develop and evaluate HPSC in practice. The logic model was established through qualitative, iterative methods in the ...

Introduction: Implementation science methods and a theory-driven approach can enhance the understanding of whether, how, and why integrated care for frail older adults is successful in practice. In this study, we aimed to perform a contextual analysis, develop a logic model, and select preliminary implementation strategies for an integrated care model in newly created information and advice centers for older adults in Canton Basel-Landschaft, ...

Many combinatorial problems exhibit symmetry, a transformational property that does not fundamentally alter the nature of a problem. For instance, renaming a set of identical trucks in a routing problem, mirroring or rotating a chessboard onto itself, or the automorphisms of an input graph give rise to symmetry. These symmetry properties often hinder an algorithm solving a combinatorial problem, as it wastefully investigates different ...

The difficulty of a user query can affect the performance of Information Retrieval (IR) systems. This work presents a formal model for quantifying and reasoning about query difficulty as follows: Query difficulty is considered to be a subjective belief, which is formulated on the basis of various types of evidence. This allows us to define a belief model and a set of operators for combining evidence of query difficulty. The belief model uses ...

The model expansion (MX) search problem consists of finding models of a given theory that expand a given finite interpretation. Model expansion in classical first-order logic (FO) has been proposed as the basis for an Answer Set Programming-like declarative programming framework for solving NP problems. In this paper, we present IDP, a system for solving MX problems that integrates technology from ASP and SAT. Its strenght lies both in its ...

An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modeling of so-called epistemic uncertainty, the uncertainty about the model itself. Accurately quantifying this model uncertainty is paramount to robust inference, learning and ultimately decision making. We introduce BetaProbLog, a PLP language that can model epistemic uncertainty. BetaProbLog has sound semantics, an effective inference ...

The service system is the basic abstraction of Service Science. This paper proposes the Resource-Service-System model as a conceptual model of service systems interacting in service exchanges, assuming a service-dominant logic economic worldview. The paper explains how the model was developed starting from the Resource-Event-Agent business model ontology, taking into account insights gained from studying Service Science literature and existing ...

© 2014 Elsevier Ltd. In this paper, a new method based on Fuzzy theory is presented to estimate the occurrence possibility of vapor cloud explosion (VCE) of flammable materials. This new method helps the analyst to overcome some uncertainties associated with estimating VCE possibility with the Event Tree (ET) technique. In this multi-variable model, the physical properties of the released material and the characteristics of the surrounding ...

Digital CMOS circuits are praised because of their noise immunity. However, lowering power supply voltages and shrinking device sizes, in combination with the rising electromagnetic pollution, have made this statement no longer true. An accurate behavioral model is presented for the analog simulation of digital logic circuits. The model building is automated and scalable in the sense that it allows a tradeoff between model-building speed and ...

Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). Over the last decade, building on advances in the areas of knowledge compilation and weighted model counting has drastically increased the scalability of inference in probabilistic logic programs. In this paper, we provide an overview of how this has been possible and point out some open challenges.