Collaborative dispatching allows several dispatchers to view the routing solution as a dynamic model where changes to the vehicle routes can be made in real-time. In this paper we discuss implications of collaborative dispatching on real-time decision support tools for motor carriers. We present a collaborative dispatching system which uses real-time information obtained from a telematics system. Messages sent from the vehicles are automatically analysed and actual data, such as exact arrival and departure times, as well as discrepancies between actual and planned data are identified. The collaborative dispatching system not only allows several dispatchers to concurrently modify the schedule, but also a dynamic optimisation method. The optimisation method is capable of taking into account that input data may change at any time and that dispatchers can concurrently modify the schedule and may add or relax certain constraints relevant to the optimisation model.

Case-based reasoning is deemed an important technology to alleviate the bottleneck of knowledge acquisition in Artificial Intelligence (AI). In case-based reasoning, knowledge is represented in the form of particular cases with an appropriate similarity measure rather than any form of rules. The case-based reasoning paradigm adopts the view that an Al system is dynamically changing during its life-cycle which immediately leads to learning considerations. Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strengths as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containmet decision lists, since they seem particularly tailored to case-based knowledge processing.

Functional Reactive Programming (FRP) offers a declarative way to express reactive systems such as animations, user interfaces and games. Various topics related to FRP like optimization, generalization and debugging were studied. However, the use of FRP in distributed systems has not been investigated extensively. Focused on the use of the Dunai FRP library implemented in the Haskell programming language, the aim of this thesis is to develop and evaluate a way to apply FRP to distributed systems. A library is implemented to extend Dunai with means to create distributed systems. There is support for the Client/Server network architectural model and algorithms to synchronize applications across a network. As the synchronization of distributed systems has been a topic of research for decades, this thesis explores whether developed ideas, such as Time Warp (Jefferson, 1985), can be expressed in FRP. Additionally, Client Side Prediction (Bernier, 2001) and Dead Reckoning (DIS Steering Committee, 1994) are used to predict server reactions on client-side. An exemplary application demonstrates the implementation. The application is then evaluated in a performance test and a user test. TimeWarp synchronization has a significant impact on performance. Despite this, the application is playable up to a latency of 100 ms. The result of Dead Reckoning is acceptable, whereas Client Side Prediction is not usable. The thesis shows that the developed way can be used to run FRP on distributed systems. Further work should focus the performance to enable more complex applications. Moreover, Dead Reckoning can be improved to a smoother result. Non-trivial changes are necessary to make Client Side Prediction usable.:1 Introduction ... 1 2 Functional Reactive Programming ... 4 2.1 The Arrow Type Class ... 5 2.2 The Dunai FRP Library ... 7 2.2.1 Monadic Stream Functions ... 7 2.2.2 Combining Monads ... 8 2.2.3 Monads in Monadic Stream Functions ... 9 2.3 The BearRiver FRP Library ... 10 2.4 Executing FRP ... 11 3 Synchronization of Distributed Systems ... 13 3.1 Distributed Systems ... 13 3.2 Consistency of Distributed Systems ... 14 3.3 Client/Server Architecture ... 15 3.4 Time Warp Synchronization ... 16 3.5 Dead Reckoning ... 17 3.6 Client Side Prediction ... 19 4 Cloud Haskell ... 21 4.1 Exchanging Messages ... 22 4.2 Fault Tolerance ... 23 4.3 The Cloud Haskell Platform ... 23 5 Concept ... 25 5.1 Concept of the Implemented Library ... 25 5.2 Concept of the Sample Application ... 27 5.3 Functional Requirements ... 28 5.4 Non-Functional Requirements ... 30 6 Implementation ... 32 6.1 Client/Server Architecture ... 33 6.1.1 Establishing Connections between Clients and Servers ... 33 6.1.2 Distributed Execution of FRP ... 35 6.1.3 Implementation of Servers ... 36 6.1.4 Implementation of Clients ... 38 6.2 Time Warp Synchronization ... 39 6.2.1 Rollbacks of Monadic Stream Functions ... 39 6.2.2 Executing FRP using Time Warp Synchronization ... 41 6.3 Dead Reckoning ... 45 6.4 Client Side Prediction ... 46 7 Evaluation ... 48 7.1 Evaluation of Functional Requirements ... 48 7.2 Concept of the Performance Test ... 52 7.3 Concept of the User Test ... 53 7.4 Results of the Performance Test ... 54 7.5 Results of the User Test ... 56 8 Summary ... 60 Bibliography ... A Listings ... M List of Figures ... O List of Tables ... P Glossary .. Q Abbreviations ... U

Mining for association rules and frequent patterns is a central activity in data mining. However, most existing algorithms are only moderately suitable for real-world scenarios. Most strategies use parameters like minimum support, for which it can be very difficult to define a suitable value for unknown datasets. Since most untrained users are unable or unwilling to set such technical parameters, we address the problem of replacing the minimum-support parameter with top-n strategies. In our paper, we start by extending a top-n implementation of the ECLAT algorithm to improve its performance by using heuristic search strategy optimizations. Also, real-world datasets are often distributed and modern database architectures are switching from expensive SMPs to cheaper shared-nothing blade servers. Thus, most mining queries require distribution handling. Since partitioning can be forced by user-defined semantics, it is often forbidden to transform the data. Therefore, we developed an adaptive top-n frequent-pattern mining algorithm that simplifies the mining process on real distributions by relaxing some requirements on the results. We first combine the PARTITION and the TPUT algorithms to handle distributed top-n frequent-pattern mining. Then, we extend this new algorithm for distributions with real-world data characteristics. For frequent-pattern mining algorithms, equal distributions are important conditions, and tiny partitions can cause performance bottlenecks. Hence, we implemented an approach called MAST that defines a minimum absolute-support threshold. MAST prunes patterns with low chances of reaching the global top-n result set and high computing costs. In total, our approach simplifies the process of frequent-pattern mining for real customer scenarios and data sets. This may make frequent-pattern mining accessible for very new user groups. Finally, we present results of our algorithms when run on the SAP NetWeaver BW Acceleratorwith standard and real business datasets.

Die Begriffe der künstlichen Intelligenz und ihrer zentralen Komponente, der Wissensverarbeitung, werden aus der Sicht eines Informatikers der 1980er Jahre konzeptionell beschrieben. Nach dem Aufzeigen prinzipieller Möglichkeiten und Grenzen der Technologie erfolgt die Diskussion zu Fragestellungen im weltanschaulichen und gesellschaftlichen Kontext.:1. Einleitung 2. Was ist künstliche Intelligenz? 3. Wissensverarbeitung als Grundlage der künstlichen Intelligenz 4. Möglichkeiten und Grenzen der Wissensverarbeitung 5. Philosophische Probleme und soziale Auswirkungen der künstlichen Intelligenz 6. Schlussbemerkungen

Das Anliegen der Arbeit besteht in der Entwicklung eines Wissensrepräsentationsmodells, das sich insbesondere für die Beschreibung komplex strukturierter Objekte eignet. Den Ausgangspunkt bildet eine Charakterisierung der Problematik der Wissensrepräsentation. Aus der Darstellung eines für das Gebiet der rechnergestützten Konstruktion typischen Diskursbereiches Getriebekonstruktion lassen sich Anforderungen an Modelle zur Beschreibung komplex strukturierter Objekte in Wissensbasen ableiten. Der Hauptteil der Arbeit besteht in der Entwicklung eines funktionalen Wissensrepräsentationsmodells, das diesen Anforderungen gerecht wird. Das Modell ermöglicht gleichzeitig eine effiziente Implementation wissensbasierter Systeme auf der Grundlage der Programmiersprache LISP sowie das Herstellen von Beziehungen zu Datenmodellen einerseits und Wissensrepräsentationsmodellen, insbesondere der Prädikatenlogik erster Ordnung, andererseits. Unter Bezugnahme auf die Datenbanktechnologie wird die Struktur von Wissensbanksystemen beschrieben. Ein wesentlicher Aspekt der Arbeit besteht im Aufzeigen der Möglichkeit und des Weges, das Wissen eines Konstrukteurs zu formalisieren und in eine Wissensbasis abzubilden.:1. Einleitung 2. Wissensrepräsentation in technischen Systemen 3. Beispielsdiskursbereiche 4. Funktionales Wissensrepräsentationsmodell 5. Beziehungen zwischen Prädikatenlogik erster Ordnung und funktionalem Wissensrepräsentationsmodell 6. Aufbau von Wissensbanksystemen 7. Anwendung des funktionalen Wissensrepräsentationsmodells für die Implementation wissensbasierter Systeme 8. Schlussbemerkungen

Common Machine-Learning (ML) approaches for scene classification require a large amountof training data. However, for classification of depth sensor data, in contrast to image data, relativelyfew databases are publicly available and manual generation of semantically labeled 3D point clouds isan even more time-consuming task. To simplify the training data generation process for a wide rangeof domains, we have developed theBLAINDERadd-on package for the open-source 3D modelingsoftware Blender, which enables a largely automated generation of semantically annotated point-cloud data in virtual 3D environments. In this paper, we focus on classical depth-sensing techniquesLight Detection and Ranging (LiDAR) and Sound Navigation and Ranging (Sonar). Within theBLAINDERadd-on, different depth sensors can be loaded from presets, customized sensors can beimplemented and different environmental conditions (e.g., influence of rain, dust) can be simulated.The semantically labeled data can be exported to various 2D and 3D formats and are thus optimizedfor different ML applications and visualizations. In addition, semantically labeled images can beexported using the rendering functionalities of Blender.

A software concept is described combining computer graphics and artificial intelligence to support practical graphic systems to check, correct or generate their spatiotemporal objects with the help of knowledge-based inferences. The unified approach is demonstrated at four quite different applications. / Ein Softwarekonzept wird beschrieben, das Computergrafik und Künstliche Intelligenz kombiniert, um praktische Grafiksysteme beim Überprüfen, Korrigieren oder Generieren ihrer raumzeitlichen Objekte mit Hilfe von wissensbasierten Inferenzen zu unterstützen. Das einheitliche Verfahren wird an vier ganz unterschiedlichen Anwendungen demonstriert.