# Marco WilhelmTechnische Universität Dortmund | TUD · Faculty of Computer Science

Diplom

12
Publications
230
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15
Citations
Introduction
I'm a PhD student at the Department of Computer Science of the TU Dortmund University in Dortmund, Germany. My current research focusses on probabilistic Description Logics, in particular, and knowledge engineering, in general.

## Publications

Publications (12)
Article
Many modern artificial intelligence (AI) systems like human-interacting smart devices or expert systems adapt to specific users' information processes but the underlying AI methods commonly lack a theory of mind. Thus, there is a need to better understand human thinking and to integrate the resulting cognitive models into AI methods. By taking the...
Preprint
Full-text available
Activation-based conditional inference applies conditional reasoning to ACT-R, a cognitive architecture developed to formalize human reasoning. The idea of activation-based conditional inference is to determine a reasonable subset of a conditional belief base in order to draw inductive inferences in time. Central to activation-based conditional inf...
Chapter
The probabilistic Description Logic is an extension of the Description Logic that allows for uncertain conditional statements of the form “if C holds, then D holds with probability p,” together with probabilistic assertions about individuals. In , probabilities are understood as an agent’s degree of belief. Probabilistic conditionals are formally i...
Chapter
The probabilistic Description Logic extends the classical Description Logic with probabilistic conditionals of the form (D|C)[p] stating that “D follows from C with probability p.” Conditionals are interpreted based on the aggregating semantics where probabilities are understood as degrees of belief. For reasoning with probabilistic conditional kno...
Chapter
We present $$\mathcal {ALC}^\mathsf {ME}$$, a probabilistic variant of the Description Logic $$\mathcal {ALC}$$ that allows for representing and processing conditional statements of the form “if E holds, then F follows with probability p” under the principle of maximum entropy. Probabilities are understood as degrees of belief and formally interpre...
Chapter
Probabilistic reasoning under the principle of maximum entropy (so-called MaxEnt principle) is a viable and convenient alternative to graph-based methodologies such as Bayesian networks that realises an idea of information economy, i.e., of being as unbiased as possible. For relational conditional knowledge, the aggregating semantics provides a sem...
Conference Paper
First-order typed model counting extends first-order model counting by the ability to distinguish between different types of models. In this paper, we exploit this benefit in order to calculate weighted conditional impacts (WCIs) which play a central role in nonmonotonic reasoning based on conditionals. More precisely, WCIs store information about...
Conference Paper
Ranking functions constitute a powerful formalism for nonmonotonic reasoning based on qualitative conditional knowledge. Conditionals are formalized defeasible rules and thus allow one to express that certain individuals or subclasses of some broader concept behave differently. More precisely, in order to model these exceptions by means of ranking...
Article
An often used methodology for reasoning with probabilistic conditional knowledge bases is provided by the principle of maximum entropy (so-called MaxEnt principle) that realises an idea of least amount of assumed information and thus of being as unbiased as possible. In this paper we exploit the fact that MaxEnt distributions can be computed by sol...
Article
Probabilistic reasoning under the so-called principle of maximum entropy is a viable and convenient alternative to Bayesian networks, relieving the user from providing complete (local) probabilistic information and observing rigorous conditional independence assumptions. In this paper, we present a novel approach to performing computational MaxEnt...

## Projects

Project (1)
Project
The aim of this research unit is to integrate both qualitative and quantitative forms of reasoning, resulting in hybrid reasoning formalisms.