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Die nächtliche (24-stündige) Überwachung des intraokularen Drucks (IOD) bei stationären Glaukompatienten wird in Europa seit mehr als 100 Jahren eingesetzt, um Spitzenwerte zu erkennen, die während der regulären Sprechstundenzeiten übersehen werden. Daten, die diese Praxis unterstützen, fehlen, zum Teil weil es schwierig ist, manuell erstellte IOD-Kurven mit dem objektiven Verlauf des Glaukoms zu korrelieren. Um dieses Problem zu beheben, haben wir automatisierte IOD-Datenextraktionswerkzeuge eingesetzt und auf eine Korrelation mit einem fortschreitenden Verlust der retinalen Nervenfaserschicht auf der optischen Kohärenztomographie im Spektralbereich (SDOCT) getestet.
Large volumes of data are collected today in many domains. Often, there is so much data available, that it is difficult to identify the relevant pieces of information. Knowledge discovery seeks to obtain novel, interesting and useful information from large datasets.
One key technique for that purpose is subgroup discovery. It aims at identifying descriptions for subsets of the data, which have an interesting distribution with respect to a predefined target concept. This work improves the efficiency and effectiveness of subgroup discovery in different directions.
For efficient exhaustive subgroup discovery, algorithmic improvements are proposed for three important variations of the standard setting: First, novel optimistic estimate bounds are derived for subgroup discovery with numeric target concepts. These allow for skipping the evaluation of large parts of the search space without influencing the results. Additionally, necessary adaptations to data structures for this setting are discussed. Second, for exceptional model mining, that is, subgroup discovery with a model over multiple attributes as target concept, a generic extension of the well-known FP-tree data structure is introduced. The modified data structure stores intermediate condensed data representations, which depend on the chosen model class, in the nodes of the trees. This allows the application for many popular model classes. Third, subgroup discovery with generalization-aware measures is investigated.
These interestingness measures compare the target share or mean value in the subgroup with the respective maximum value in all its generalizations. For this setting, a novel method for deriving optimistic estimates is proposed. In contrast to previous approaches, the novel measures are not exclusively based on the anti-monotonicity of instance coverage, but also takes the difference of coverage between the subgroup and its generalizations into account. In all three areas, the advances lead to runtime improvements of more than an order of magnitude.
The second part of the contributions focuses on the \emph{effectiveness} of subgroup discovery. These improvements aim to identify more interesting subgroups in practical applications. For that purpose, the concept of expectation-driven subgroup discovery is introduced as a new family of interestingness measures. It computes the score of a subgroup based on the difference between the actual target share and the target share that could be expected given the statistics for the separate influence factors that are combined to describe the subgroup.
In doing so, previously undetected interesting subgroups are discovered, while other, partially redundant findings are suppressed.
Furthermore, this work also approaches practical issues of subgroup discovery: In that direction, the VIKAMINE II tool is presented, which extends its predecessor with a rebuild user interface, novel algorithms for automatic discovery, new interactive mining techniques, as well novel options for result presentation and introspection. Finally, some real-world applications are described that utilized the presented techniques. These include the identification of influence factors on the success and satisfaction of university students and the description of locations using tagging data of geo-referenced images.
Recent advances in Natural Language Preprocessing (NLP) allow for a fully automatic extraction of character networks for an incoming text. These networks serve as a compact and easy to grasp representation of literary fiction. They offer an aggregated view of the text, which can be used during distant reading approaches for the analysis of literary hypotheses. In their core, the networks consist of nodes, which represent literary characters, and edges, which represent relations between characters. For an automatic extraction of such a network, the first step is the detection of the references of all fictional entities that are of importance for a text. References to the fictional entities appear in the form of names, noun phrases and pronouns and prior to this work, no components capable of automatic detection of character references were available. Existing tools are only capable of detecting proper nouns, a subset of all character references. When evaluated on the task of detecting proper nouns in the domain of literary fiction, they still underperform at an F1-score of just about 50%. This thesis uses techniques from the field of semi-supervised learning, such as Distant supervision and Generalized Expectations, and improves the results of an existing tool to about 82%, when evaluated on all three categories in literary fiction, but without the need for annotated data in the target domain. However, since this quality is still not sufficient, the decision to annotate DROC, a corpus comprising 90 fragments of German novels was made. This resulted in a new general purpose annotation environment titled as ATHEN, as well as annotated data that spans about 500.000 tokens in total. Using this data, the combination of supervised algorithms and a tailored rule based algorithm, which in combination are able to exploit both - local consistencies as well as global consistencies - yield an algorithm with an F1-score of about 93%. This component is referred to as the Kallimachos tagger.
A character network can not directly display references however, instead they need to be clustered so that all references that belong to a real world or fictional entity are grouped together. This process widely known as coreference resolution is a hard problem in the focus of research for more than half a century. This work experimented with adaptations of classical feature based machine learning, with a dedicated rule based algorithm and with modern techniques of Deep Learning, but no approach can surpass 55% B-Cubed F1, when evaluated on DROC. Due to this barrier, many researchers do not use a fully-fledged coreference resolution when they extract character networks, but only focus on a more forgiving subset- the names. For novels such as Alice's Adventures in Wonderland by Lewis Caroll, this would however only result in a network in which many important characters are missing. In order to integrate important characters into the network that are not named by the author, this work makes use of automatic detection of speaker and addressees for direct speech utterances (all entities involved in a dialog are considered to be of importance). This problem is by itself not an easy task, however the most successful system analysed in this thesis is able to correctly determine the speaker to about 85% of the utterances as well as about 65% of the addressees. This speaker information can not only help to identify the most dominant characters, but also serves as a way to model the relations between entities.
During the span of this work, components have been developed to model relations between characters using speaker attribution, using co-occurrences as well as by the usage of true interactions, for which yet again a dataset was annotated using ATHEN. Furthermore, since relations between characters are usually typed, a component for the extraction of a typed relation was developed. Similar to the experiments for the character reference detection, a combination of a rule based and a Maximum Entropy classifier yielded the best overall results, with the extraction of family relations showing a score of about 80% and the quality of love relations with a score of about 50%. For family relations, a kernel for a Support Vector Machine was developed that even exceeded the scores of the combined approach but is behind on the other labels.
In addition, this work presents new ways to evaluate automatically extracted networks without the need of domain experts, instead it relies on the usage of expert summaries. It also refrains from the uses of social network analysis for the evaluation, but instead presents ranked evaluations using Precision@k and the Spearman Rank correlation coefficient for the evaluation of the nodes and edges of the network. An analysis using these metrics showed, that the central characters of a novel are contained with high probability but the quality drops rather fast if more than five entities are analyzed. The quality of the edges is mainly dominated by the quality of the coreference resolution and the correlation coefficient between gold edges and system edges therefore varies between 30 and 60%.
All developed components are aggregated alongside a large set of other preprocessing modules in the Kallimachos pipeline and can be reused without any restrictions.
In recent years, great progress has been made in the area of Artificial Intelligence (AI) due to the possibilities of Deep Learning which steadily yielded new state-of-the-art results especially in many image recognition tasks.
Currently, in some areas, human performance is achieved or already exceeded.
This great development already had an impact on the area of Optical Music Recognition (OMR) as several novel methods relying on Deep Learning succeeded in specific tasks.
Musicologists are interested in large-scale musical analysis and in publishing digital transcriptions in a collection enabling to develop tools for searching and data retrieving.
The application of OMR promises to simplify and thus speed-up the transcription process by either providing fully-automatic or semi-automatic approaches.
This thesis focuses on the automatic transcription of Medieval music with a focus on square notation which poses a challenging task due to complex layouts, highly varying handwritten notations, and degradation.
However, since handwritten music notations are quite complex to read, even for an experienced musicologist, it is to be expected that even with new techniques of OMR manual corrections are required to obtain the transcriptions.
This thesis presents several new approaches and open source software solutions for layout analysis and Automatic Text Recognition (ATR) for early documents and for OMR of Medieval manuscripts providing state-of-the-art technology.
Fully Convolutional Networks (FCN) are applied for the segmentation of historical manuscripts and early printed books, to detect staff lines, and to recognize neume notations.
The ATR engine Calamari is presented which allows for ATR of early prints and also the recognition of lyrics.
Configurable CNN/LSTM-network architectures which are trained with the segmentation-free CTC-loss are applied to the sequential recognition of text but also monophonic music.
Finally, a syllable-to-neume assignment algorithm is presented which represents the final step to obtain a complete transcription of the music.
The evaluations show that the performances of any algorithm is highly depending on the material at hand and the number of training instances.
The presented staff line detection correctly identifies staff lines and staves with an $F_1$-score of above $99.5\%$.
The symbol recognition yields a diplomatic Symbol Accuracy Rate (dSAR) of above $90\%$ by counting the number of correct predictions in the symbols sequence normalized by its length.
The ATR of lyrics achieved a Character Error Rate (CAR) (equivalently the number of correct predictions normalized by the sentence length) of above $93\%$ trained on 771 lyric lines of Medieval manuscripts and of 99.89\% when training on around 3.5 million lines of contemporary printed fonts.
The assignment of syllables and their corresponding neumes reached $F_1$-scores of up to $99.2\%$.
A direct comparison to previously published performances is difficult due to different materials and metrics.
However, estimations show that the reported values of this thesis exceed the state-of-the-art in the area of square notation.
A further goal of this thesis is to enable musicologists without technical background to apply the developed algorithms in a complete workflow by providing a user-friendly and comfortable Graphical User Interface (GUI) encapsulating the technical details.
For this purpose, this thesis presents the web-application OMMR4all.
Its fully-functional workflow includes the proposed state-of-the-art machine-learning algorithms and optionally allows for a manual intervention at any stage to correct the output preventing error propagation.
To simplify the manual (post-) correction, OMMR4all provides an overlay-editor that superimposes the annotations with a scan of the original manuscripts so that errors can easily be spotted.
The workflow is designed to be iteratively improvable by training better models as soon as new Ground Truth (GT) is available.
Die Extraktion von Metadaten aus historischen Dokumenten ist eine zeitintensive, komplexe und höchst fehleranfällige Tätigkeit, die üblicherweise vom menschlichen Experten übernommen werden muss. Sie ist jedoch notwendig, um Bezüge zwischen Dokumenten herzustellen, Suchanfragen zu historischen Ereignissen korrekt zu beantworten oder semantische Verknüpfungen aufzubauen. Um den manuellen Aufwand dieser Aufgabe reduzieren zu können, sollen Verfahren der Named Entity Recognition angewendet werden. Die Klassifikation von Termen in historischen Handschriften stellt jedoch eine große Herausforderung dar, da die Domäne eine hohe Schreibweisenvarianz durch unter anderem nur konventionell vereinbarte Orthographie mit sich bringt. Diese Arbeit stellt Verfahren vor, die auch in komplexen syntaktischen Umgebungen arbeiten können, indem sie auf Informationen aus dem Kontext der zu klassifizierenden Terme zurückgreifen und diese mit domänenspezifischen Heuristiken kombinieren. Weiterhin wird evaluiert, wie die so gewonnenen Metadaten genutzt werden können, um in Workflow-Systemen zur Digitalisierung historischer Handschriften Mehrwerte durch Heuristiken zur Produktionsfehlererkennung zu erzielen.
Today knowledge base authoring for the engineering of intelligent systems is performed mainly by using tools with graphical user interfaces. An alternative human-computer interaction para- digm is the maintenance and manipulation of electronic documents, which provides several ad- vantages with respect to the social aspects of knowledge acquisition. Until today it hardly has found any attention as a method for knowledge engineering.
This thesis provides a comprehensive discussion of document-centered knowledge acquisition with knowledge markup languages. There, electronic documents are edited by the knowledge authors and the executable knowledge base entities are captured by markup language expressions within the documents. The analysis of this approach reveals significant advantages as well as new challenges when compared to the use of traditional GUI-based tools.
Some advantages of the approach are the low barriers for domain expert participation, the simple integration of informal descriptions, and the possibility of incremental knowledge for- malization. It therefore provides good conditions for building up a knowledge acquisition pro- cess based on the mixed-initiative strategy, being a flexible combination of direct and indirect knowledge acquisition. Further it turns out that document-centered knowledge acquisition with knowledge markup languages provides high potential for creating customized knowledge au- thoring environments, tailored to the needs of the current knowledge engineering project and its participants. The thesis derives a process model to optimally exploit this customization po- tential, evolving a project specific authoring environment by an agile process on the meta level. This meta-engineering process continuously refines the three aspects of the document space: The employed markup languages, the scope of the informal knowledge, and the structuring and organization of the documents. The evolution of the first aspect, the markup languages, plays a key role, implying the design of project specific markup languages that are easily understood by the knowledge authors and that are suitable to capture the required formal knowledge precisely. The goal of the meta-engineering process is to create a knowledge authoring environment, where structure and presentation of the domain knowledge comply well to the users’ mental model of the domain. In that way, the approach can help to ease major issues of knowledge-based system development, such as high initial development costs and long-term maintenance problems.
In practice, the application of the meta-engineering approach for document-centered knowl- edge acquisition poses several technical challenges that need to be coped with by appropriate tool support. In this thesis KnowWE, an extensible document-centered knowledge acquisition environment is presented. The system is designed to support the technical tasks implied by the meta-engineering approach, as for instance design and implementation of new markup lan- guages, content refactoring, and authoring support. It is used to evaluate the approach in several real-world case-studies from different domains, such as medicine or engineering for instance.
We end the thesis by a summary and point out further interesting research questions consid- ering the document-centered knowledge acquisition approach.
Bei Lernprozessen spielt das Anwenden der zu erlernenden Tätigkeit eine wichtige Rolle. Im Kontext der Ausbildung an Schulen und Hochschulen bedeutet dies, dass es wichtig ist, Schülern und Studierenden ausreichend viele Übungsmöglichkeiten anzubieten. Die von Lehrpersonal bei einer "Korrektur" erstellte Rückmeldung, auch Feedback genannt, ist jedoch teuer, da der zeitliche Aufwand je nach Art der Aufgabe beträchtlich ist.
Eine Lösung dieser Problematik stellen E-Learning-Systeme dar. Geeignete Systeme können nicht nur Lernstoff präsentieren, sondern auch Übungsaufgaben anbieten und nach deren Bearbeitung quasi unmittelbar entsprechendes Feedback generieren. Es ist jedoch im Allgemeinen nicht einfach, maschinelle Verfahren zu implementieren, die Bearbeitungen von Übungsaufgaben korrigieren und entsprechendes Feedback erstellen. Für einige Aufgabentypen, wie beispielsweise Multiple-Choice-Aufgaben, ist dies zwar trivial, doch sind diese vor allem dazu gut geeignet, sogenanntes Faktenwissen abzuprüfen. Das Einüben von Lernzielen im Bereich der Anwendung ist damit kaum möglich.
Die Behandlung dieser nach gängigen Taxonomien höheren kognitiven Lernziele erlauben sogenannte offene Aufgabentypen, deren Bearbeitung meist durch die Erstellung eines Freitexts in natürlicher Sprache erfolgt. Die Information bzw. das Wissen, das Lernende eingeben, liegt hier also in sogenannter „unstrukturierter“ Form vor. Dieses unstrukturierte Wissen ist maschinell nur schwer verwertbar, sodass sich Trainingssysteme, die Aufgaben dieser Art stellen und entsprechende Rückmeldung geben, bisher nicht durchgesetzt haben. Es existieren jedoch auch offene Aufgabentypen, bei denen Lernende das Wissen in strukturierter Form eingeben, so dass es maschinell leichter zu verwerten ist. Für Aufgaben dieser Art lassen sich somit Trainingssysteme erstellen, die eine gute Möglichkeit darstellen, Schülern und Studierenden auch für praxisnahe Anwendungen viele Übungsmöglichkeiten zur Verfügung zu stellen, ohne das Lehrpersonal zusätzlich zu belasten.
In dieser Arbeit wird beschrieben, wie bestimmte Eigenschaften von Aufgaben ausgenutzt werden, um entsprechende Trainingssysteme konzipieren und implementieren zu können. Es handelt sich dabei um Aufgaben, deren Lösungen strukturiert und maschinell interpretierbar sind.
Im Hauptteil der Arbeit werden vier Trainingssysteme bzw. deren Komponenten beschrieben und es wird von den Erfahrungen mit deren Einsatz in der Praxis berichtet: Eine Komponente des Trainingssystems „CaseTrain“ kann Feedback zu UML Klassendiagrammen erzeugen. Das neuartige Trainingssystem „WARP“ generiert zu UML Aktivitätsdiagrammen Feedback in mehreren Ebenen, u.a. indem es das durch Aktivitätsdiagramme definierte Verhalten von Robotern in virtuellen Umgebungen visualisiert. Mit „ÜPS“ steht ein Trainingssystem zur Verfügung, mit welchem die Eingabe von SQL-Anfragen eingeübt werden kann. Eine weitere in „CaseTrain“ implementierte Komponente für Bildmarkierungsaufgaben ermöglicht eine unmittelbare, automatische Bewertung entsprechender Aufgaben.
Die Systeme wurden im Zeitraum zwischen 2011 und 2014 an der Universität Würzburg in Vorlesungen mit bis zu 300 Studierenden eingesetzt und evaluiert. Die Evaluierung ergab eine hohe Nutzung und eine gute Bewertung der Studierenden der eingesetzten Konzepte, womit belegt wurde, dass elektronische Trainingssysteme für offene Aufgaben in der Praxis eingesetzt werden können.
Knowledge-based systems (KBS) face an ever-increasing interest in various disciplines and contexts. Yet, the former aim to construct the ’perfect intelligent software’ continuously shifts to user-centered, participative solutions. Such systems enable users to contribute their personal knowledge to the problem solving process for increased efficiency and an ameliorated user experience. More precisely, we define non-functional key requirements of participative KBS as: Transparency (encompassing KBS status mediation), configurability (user adaptability, degree of user control/exploration), quality of the KB and UI, and evolvability (enabling the KBS to grow mature with their users). Many of those requirements depend on the respective target users, thus calling for a more user-centered development. Often, also highly expertise domains are targeted — inducing highly complex KBs — which requires a more careful and considerate UI/interaction design. Still, current KBS engineering (KBSE) approaches mostly focus on knowledge acquisition (KA) This often leads to non-optimal, little reusable, and non/little evaluated KBS front-end solutions.
In this thesis we propose a more encompassing KBSE approach. Due to the strong mutual influences between KB and UI, we suggest a novel form of intertwined UI and KB development. We base the approach on three core components for encompassing KBSE:
(1) Extensible prototyping, a tailored form of evolutionary prototyping; this builds on mature UI prototypes and offers two extension steps for the anytime creation of core KBS prototypes (KB + core UI) and fully productive KBS (core KBS prototype + common framing functionality). (2) KBS UI patterns, that define reusable solutions for the core KBS UI/interaction; we provide a basic collection of such patterns in this work. (3) Suitable usability instruments for the assessment of the KBS artifacts. Therewith, we do not strive for ’yet another’ self-contained KBS engineering methodology. Rather, we motivate to extend existing approaches by the proposed key components. We demonstrate this based on an agile KBSE model.
For practical support, we introduce the tailored KBSE tool ProKEt. ProKEt offers a basic selection of KBS core UI patterns and corresponding configuration options out of the box; their further adaption/extension is possible on various levels of expertise. For practical usability support, ProKEt offers facilities for quantitative and qualitative data collection. ProKEt explicitly fosters the suggested, intertwined development of UI and KB. For seamlessly integrating KA activities, it provides extension points for two selected external KA tools: For KnowOF, a standard office based KA environment. And for KnowWE, a semantic wiki for collaborative KA. Therewith, ProKEt offers powerful support for encompassing, user-centered KBSE.
Finally, based on the approach and the tool, we also developed a novel KBS type: Clarification KBS as a mashup of consultation and justification KBS modules. Those denote a specifically suitable realization for participative KBS in highly expertise contexts and consequently require a specific design. In this thesis, apart from more common UI solutions, we particularly also introduce KBS UI patterns especially tailored towards Clarification KBS.
An Intelligent Semi-Automatic Workflow for Optical Character Recognition of Historical Printings
(2020)
Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, Automatic Text Recognition (ATR) and postcorrection. Their major drawback is that they only offer limited applicability by non-technical users like humanist scholars, in particular when it comes to the combined use of several tools in a workflow. Furthermore, depending on the material, these tools are usually not able to fully automatically achieve sufficiently low error rates, let alone perfect results, creating a demand for an interactive postcorrection functionality which, however, is generally not incorporated.
This thesis addresses these issues by presenting an open-source OCR software called OCR4all which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required Ground Truth (GT) for training stronger mixed models (for segmentation as well as text recognition) is not available, yet, neither in the desired quantity nor quality.
To deal with this issue in the short run, OCR4all offers better recognition capabilities in combination with a very comfortable Graphical User Interface (GUI) that allows error corrections not only in the final output, but already in early stages to minimize error propagation. In the long run this constant manual correction produces large quantities of valuable, high quality training material which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. The architecture of OCR4all allows for an easy integration (or substitution) of newly developed tools for its main components by supporting standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.
In addition to OCR4all, several methodical extensions in the form of accuracy improving techniques for training and recognition are presented. Most notably an effective, sophisticated, and adaptable voting methodology using a single ATR engine, a pretraining procedure, and an Active Learning (AL) component are proposed. Experiments showed that combining pretraining and voting significantly improves the effectiveness of book-specific training, reducing the obtained Character Error Rates (CERs) by more than 50%.
The proposed extensions were further evaluated during two real world case studies: First, the voting and pretraining techniques are transferred to the task of constructing so-called mixed models which are trained on a variety of different fonts. This was done by using 19th century Fraktur script as an example, resulting in a considerable improvement over a variety of existing open-source and commercial engines and models. Second, the extension from ATR on raw text to the adjacent topic of typography recognition was successfully addressed by thoroughly indexing a historical lexicon that heavily relies on different font types in order to encode its complex semantic structure.
During the main experiments on very complex early printed books even users with minimal or no experience were able to not only comfortably deal with the challenges presented by the complex layout, but also to recognize the text with manageable effort and great quality, achieving excellent CERs below 0.5%. Furthermore, the fully automated application on 19th century novels showed that OCR4all (average CER of 0.85%) can considerably outperform the commercial state-of-the-art tool ABBYY Finereader (5.3%) on moderate layouts if suitably pretrained mixed ATR models are available.
Die vorliegende Arbeit ist in zwei Teile gegliedert, von denen der erste Teil den theoretischen Hintergrund und empirische Befunde zum Thema „Komplexes Problemlösen“ behandelt. Der zweite Teil beinhaltet Methodik und Ergebnisse der durchgeführten Untersuchung. Nach der Einleitung in Kapitel 1 werden in Kapitel 2 die „Grundkonzepte des Komplexen Problemlösens“ vorgestellt, wobei mit der Abgrenzung des Bereichs „Komplexes Problemlösen“ begonnen wird. Anschließend werden die Eigenschaften von komplexen Systemen und deren Anforderungen an Problemlöser beschrieben, wobei die Taxonomie1 von Dörner et al. (1994) zugrunde gelegt wird. In Kapitel 3 werden Modelle der Wissensrepräsentation und des Problemlösens vorgestellt. Dabei wird der Begriff der „Strategie“ diskutiert und im Zusammenhang mit verschiedenen allgemeinen Modellen des Problemlösens erläutert. Kapitel 4 behandelt das Konzept „Delegation“. Delegation wird in dieser Arbeit als Methode verwendet, um Versuchspersonen zur Formalisierung ihrer Strategien zu bewegen, wobei sie die Ausführung der Strategien gleichzeitig beobachten können. Es werden vor allem Befunde aus der Organisationspsychologie und Unternehmensführung berichtet und die Anwendung von Delegation in der Interaktion zwischen Mensch und künstlichem Agent erörtert. In Kapitel 5 werden Waldbrandsimulationen behandelt. Diese zählen zu den klassischen Simulationen, die zur Untersuchung von Komplexem Problemlösen verwendet werden. Zuerst wird auf computergestützte Simulation im Allgemeinen eingegangen, wobei Unterschiede zu traditionellen Untersuchungsmethoden angesprochen werden. Dabei wird auch die Bedeutung der Multiagentensimulation für die Komplexe Problemlöseforschung hervorgehoben. Anschließend wird Feuerverhalten und Feuerbekämpfung als Vorbild für Waldbrandsimulationen erläutert. Dadurch können sowohl Anhaltspunkte zur Beurteilung der Plausibilität als auch für die Implementierung einer Waldbrandsimulation gewonnen werden. Im Anschluss daran werden drei bekannte Beispiele für Waldbrandsimulationen vorgestellt, wobei auch auf domänen- bzw. simulationsspezifische Strategien eingegangen wird. In Kapitel 6 wird ein Überblick über verschiedene empirische Befunde aus dem Bereich des Komplexen Problemlösens gegeben. Diese betreffen sowohl Eigenschaften von komplexen Systemen als auch Merkmale des Problemlösers. In Kapitel 7 werden die wichtigsten Kritikpunkte und Probleme, mit denen die Komplexe Problemlöseforschung zu kämpfen hat, zusammengefasst. Die konkreten Fragestellungen der Untersuchung werden in Kapitel 8 vorgestellt, wobei Kapitel 9 und 10 erläutern, mit welcher Methodik diese Fragen untersucht werden. In diesem Zusammenhang wird auch die Simulationsumgebung SeSAm vorgestellt. Im folgenden Kapitel 11 wird auf die Eigenschaften der implementierten Waldbrandsimulation eingegangen. Kapitel 12 beschreibt den Aufbau und Ablauf der Untersuchung, mit der die Daten gewonnen werden, die in Kapitel 13 berichtet werden. Eine Diskussion der Befunde im Hinblick auf die Fragestellungen und ihre Bedeutung für die zukünftige Forschung erfolgt in Kapitel 14.