Institut für Informatik und Computational Science
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Institute
Workshop "Formale Methoden der Linguistik" und "14. Theorietag Automaten und Formale Sprachen"
(2004)
Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms
The power of a language L is the set of all powers of the words in L. In this paper, the following decision problem is investigated. Given a context-free language L, is the power of L context-free? We show that this problem is decidable for languages over unary alphabets, but it is undecidable whenever languages over alphabets with at least two letters are considered. (C) 2003 Elsevier B.V. All rights reserved
We present a general approach for representing and reasoning with sets of defaults in default logic, focusing on reasoning about preferences among sets of defaults. First, we consider how to control the application of a set of defaults so that either all apply (if possible) or none do (if not). From this, an approach to dealing with preferences among sets of default rules is developed. We begin with an ordered default theory, consisting of a standard default theory, but with possible preferences on sets of rules. This theory is transformed into a second, standard default theory wherein the preferences are respected. The approach differs from other work, in that we obtain standard default theories and do not rely on prioritized versions of default logic. In practical terms this means we can immediately use existing default logic theorem provers for an implementation. Also, we directly generate just those extensions containing the most preferred applied rules; in contrast, most previous approaches generate all extensions, then select the most preferred. In a major application of the approach, we show how semimonotonic default theories can be encoded so that reasoning can be carried out at the object level. With this, we can reason about default extensions from within the framework of a standard default logic. Hence one can encode notions such as skeptical and credulous conclusions, and can reason about such conclusions within a single extension
In recent years, there has been a large amount of disparate work concerning the representation and reasoning with qualitative preferential information by means of approaches to nonmonotonic reasoning. Given the variety of underlying systems, assumptions, motivations, and intuitions, it is difficult to compare or relate one approach with another. Here, we present an overview and classification for approaches to dealing with preference. A set of criteria for classifying approaches is given, followed by a set of desiderata that an approach might be expected to satisfy. A comprehensive set of approaches is subsequently given and classified with respect to these sets of underlying principles
In this paper, we show how an approach to belief revision and belief contraction can be axiomatized by means of quantified Boolean formulas. Specifically, we consider the approach of belief change scenarios, a general framework that has been introduced for expressing different forms of belief change. The essential idea is that for a belief change scenario (K, R, C), the set of formulas K, representing the knowledge base, is modified so that the sets of formulas R and C are respectively true in, and consistent with the result. By restricting the form of a belief change scenario, one obtains specific belief change operators including belief revision, contraction, update, and merging. For both the general approach and for specific operators, we give a quantified Boolean formula such that satisfying truth assignments to the free variables correspond to belief change extensions in the original approach. Hence, we reduce the problem of determining the results of a belief change operation to that of satisfiability. This approach has several benefits. First, it furnishes an axiomatic specification of belief change with respect to belief change scenarios. This then leads to further insight into the belief change framework. Second, this axiomatization allows us to identify strict complexity bounds for the considered reasoning tasks. Third, we have implemented these different forms of belief change by means of existing solvers for quantified Boolean formulas. As well, it appears that this approach may be straightforwardly applied to other specific approaches to belief change
Gerade in den letzten Jahren erfuhr Open Source Software (OSS) eine zunehmende Verbreitung und Popularität und hat sich in verschiedenen Anwendungsdomänen etabliert. Die Prozesse, welche sich im Kontext der OSS-Entwicklung (auch: OSSD – Open Source Software-Development) evolutionär herausgebildet haben, weisen in den verschiedenen OSS-Entwicklungsprojekten z.T. ähnliche Eigenschaften und Strukturen auf und auch die involvierten Entitäten, wie z.B. Artefakte, Rollen oder Software-Werkzeuge sind weitgehend miteinander vergleichbar. Dies motiviert den Gedanken, ein verallgemeinerbares Modell zu entwickeln, welches die generalisierbaren Entwicklungsprozesse im Kontext von OSS zu einem übertragbaren Modell abstrahiert. Auch in der Wissenschaftsdisziplin des Software Engineering (SE) wurde bereits erkannt, dass sich der OSSD-Ansatz in verschiedenen Aspekten erheblich von klassischen (proprietären) Modellen des SE unterscheidet und daher diese Methoden einer eigenen wissenschaftlichen Betrachtung bedürfen. In verschiedenen Publikationen wurden zwar bereits einzelne Aspekte der OSS-Entwicklung analysiert und Theorien über die zugrundeliegenden Entwicklungsmethoden formuliert, aber es existiert noch keine umfassende Beschreibung der typischen Prozesse der OSSD-Methodik, die auf einer empirischen Untersuchung existierender OSS-Entwicklungsprojekte basiert. Da dies eine Voraussetzung für die weitere wissenschaftliche Auseinandersetzung mit OSSD-Prozessen darstellt, wird im Rahmen dieser Arbeit auf der Basis vergleichender Fallstudien ein deskriptives Modell der OSSD-Prozesse hergeleitet und mit Modellierungselementen der UML formalisiert beschrieben. Das Modell generalisiert die identifizierten Prozesse, Prozessentitäten und Software-Infrastrukturen der untersuchten OSSD-Projekte. Es basiert auf einem eigens entwickelten Metamodell, welches die zu analysierenden Entitäten identifiziert und die Modellierungssichten und -elemente beschreibt, die zur UML-basierten Beschreibung der Entwicklungsprozesse verwendet werden. In einem weiteren Arbeitsschritt wird eine weiterführende Analyse des identifizierten Modells durchgeführt, um Implikationen, und Optimierungspotentiale aufzuzeigen. Diese umfassen beispielsweise die ungenügende Plan- und Terminierbarkeit von Prozessen oder die beobachtete Tendenz von OSSD-Akteuren, verschiedene Aktivitäten mit unterschiedlicher Intensität entsprechend der subjektiv wahrgenommenen Anreize auszuüben, was zur Vernachlässigung einiger Prozesse führt. Anschließend werden Optimierungszielstellungen dargestellt, die diese Unzulänglichkeiten adressieren, und ein Optimierungsansatz zur Verbesserung des OSSD-Modells wird beschrieben. Dieser Ansatz umfasst die Erweiterung der identifizierten Rollen, die Einführung neuer oder die Erweiterung bereits identifizierter Prozesse und die Modifikation oder Erweiterung der Artefakte des generalisierten OSS-Entwicklungsmodells. Die vorgestellten Modellerweiterungen dienen vor allem einer gesteigerten Qualitätssicherung und der Kompensation von vernachlässigten Prozessen, um sowohl die entwickelte Software- als auch die Prozessqualität im OSSD-Kontext zu verbessern. Desweiteren werden Softwarefunktionalitäten beschrieben, welche die identifizierte bestehende Software-Infrastruktur erweitern und eine gesamtheitlichere, softwaretechnische Unterstützung der OSSD-Prozesse ermöglichen sollen. Abschließend werden verschiedene Anwendungsszenarien der Methoden des OSS-Entwicklungsmodells, u.a. auch im kommerziellen SE, identifiziert und ein Implementierungsansatz basierend auf der OSS GENESIS vorgestellt, der zur Implementierung und Unterstützung des OSSD-Modells verwendet werden kann.
Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances
PCG-Agreement Dokument
(2004)
Motivation: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data. Results: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states