Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Abstract. We describe our previous and current efforts towards achiev-ing an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration. Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this

Entropy-based machine learning algorithms applied to genomics and pattern recognition

Transcription factors (TF) are proteins that interact with DNA to regulate the transcription of DNA to RNA and play key roles in both healthy and cancerous cells. Thus, gaining a deeper understanding of the biological factors underlying transcription factor (TF) binding specificity is important for understanding the mechanism of oncogenesis. As large, biological datasets become more readily available, machine learning (ML) algorithms have proven to make up an important and useful set of tools for cancer researchers. However, there remain many areas for potential improvements for these ML models, including a higher degree of model interpretability and overall accuracy. In this thesis, we present decision tree (DT) methods applied to DNA sequence analysis that result in highly interpretable and accurate predictions. We propose a boosted decision tree (BDT) model using the binary counts of important DNA motifs to predict the binding specificity of TFs belonging to the same protein family of binding similar DNA sequences. We then proceed to introduce a novel application of Convolutional Decision Trees (CDT) and demonstrate that this approach has distinct advantages over the BDT modeil while still accurately predicting the binding specificty of TFs. The CDT models are trained using the Cross Entropy (CE) optimization method, a Monte Carlo optimization method based on concepts from information theory related to statistical mechanics. We then further study the CDT model as a general pattern recognition and transfer learning technique and demonstrate that this approach can learn translationally invariant patterns that lead to high classification accuracy while remaining more interpretable and learning higher quality convolutional filters compared to convolutional neural networks (CNN)

Data mining methodologies for supporting engineers during system identification

Data alone are worth almost nothing. While data collection is increasing exponentially worldwide, a clear distinction between retrieving data and obtaining knowledge has to be made. Data are retrieved while measuring phenomena or gathering facts. Knowledge refers to data patterns and trends that are useful for decision making. Data interpretation creates a challenge that is particularly present in system identification, where thousands of models may explain a given set of measurements. Manually interpreting such data is not reliable. One solution is to use data mining. This thesis thus proposes an integration of techniques from data mining, a field of research where the aim is to find knowledge from data, into an existing multiple-model system identification methodology. It is shown that, within a framework for decision support, data mining techniques constitute a valuable tool for engineers performing system identification. For example, clustering techniques group similar models together in order to guide subsequent decisions since they might indicate possible states of a structure. A main issue concerns the number of clusters, which, usually, is unknown. For determining the correct number of clusters in data and estimating the quality of a clustering algorithm, a score function is proposed. The score function is a reliable index for estimating the number of clusters in a given data set, thus increasing understanding of results. Furthermore, useful information for engineers who perform system identification is achieved through the use of feature selection techniques. They allow selection of relevant parameters that explain candidate models. The core algorithm is a feature selection strategy based on global search. In addition to providing information about the candidate model space, data mining is found to be a valuable tool for supporting decisions related to subsequent sensor placement. When integrated into a methodology for iterative sensor placement, clustering is found to provide useful support through providing a rational basis for decisions related to subsequent sensor placement on existing structures. Greedy and global search strategies should be selected according to the context. Experiments show that whereas global search is more efficient for initial sensor placement, a greedy strategy is more suitable for iterative sensor placement

Improving reliability and performance of telecommunications systems by using autonomic, self-learning and self-adaptive systems

Meine Dissertation beschaeftigt sich mit autonomen, selbst-lernenden und selbst-adaptiven Systemen. Prinzipiell muss ein autonomes und selbst-lernendes System seinen eigenen Status, sowie die externen Operationen kennen, muss Systemveraenderungen erkennen koennen und muss in der Lage sein sich selbst zu adaptieren. Verbesserung der Zuverlaessigkeit von Multimedia Kommunikation: Im Zuge des Testens eines kommerziellen VoIP Servers wurde deutlich, dass das SIP Protokoll, welches fuer die Initiierung von VoIP Telefonaten verwendet wird, in einem sehr offenen Standard definiert ist. Fuer eine korrekte SIP Nachricht sind nur einige wenige Informationen notwendig. Es gibt allerdings eine enorme Anzahl an optionalen Informationen, die ebenfalls innerhalb einer SIP Nachricht verwendet werden koennen. Diese Tatsache fuehrt dazu, dass VoIP Geraete eine enorme Anzahl an unterschiedlichen SIP-Dialekten verwenden, die aus der riesigen Anzahl an unterschiedlichen Parameterkombinationen entstehen. Dies kann zu dem Problem fuehren, dass Telefone die dasselbe Protokoll verwenden, trotzdem nicht in der Lage sind\ud miteinander zu kommunizieren. Deshalb wird ein autonomes, selbst-lernendes SIP-Uebersetzungstool praesentiert, welches die Rate der faelschlich vom Server abgewiesenen SIP Nachrichten drastisch reduziert, indem ankommende Nachrichten analysiert und eventuell veraendert werden. Autonome Adaption von Systemparametern, um die Systemperformance zu verbessern: Die Performance eines kommerziellen Systems, welches Daten von unterschiedlichen mobilen Geraeten sammelt und verarbeitet, ist aufgrund des hohen ankommenden Datenaufkommens extrem wichtig. Ankommende Datentickets wandern durch ein Warteschlangensystem, wo in jedem durchlaufenen Knoten unterschiedliche atomare Aktionen durchgefuehrt werden. Dieser Aufbau ermoeglicht es, die einzelnen Knoten zu parallelisieren, in dem mehrere Auspraegungen der Knoten auf unterschiedlichen CPU-Kernen gestartet werden. Mit Hilfe eines Systems, welches analytische Ansaetze, Messungen und Simulationen verwendet, wird die optimale Softwarekonfiguration fuer eine bestimmte Hardware automatisiert gefunden. Dadurch passt sich die Software immer exakt an die aktuelle Hardware und an das aktuelle Datenaufkommen an. Die Performance des Gesamtsystems kann so drastisch verbessert werden.My dissertation will be about autonomic, self-learning and self-adaptive sys- tems. Usually an autonomic and self-learning system must be able to know its own status and the external operations, must be able to monitor system changes and must be able to self-adapt to them. Within this area my disser- tation will present two case studies of autonomic and self-learning systems. Improving reliability of multimedia communication: While testing a commercial VoIP server it became obvious that the SIP proto- col, used to initiate VoIP calls, is defined in a very open standard. That fact results in a great number of different SIP dialects, leading to the problem that some VoIP devices (hard and soft phones) may not be able to communicate with each other, even though they use the same protocol. Therefore an au- tonomic, self-learning SIP translator will be presented, that will decrease the rate of rejected SIP messages. Automatic adaptation of system parameters to improve system performance: The performance of a commercial system that collects data from various mo- bile devices is critical, because of the high amount of incoming data. There- fore performance tests will be initiated and automatically evaluated. Through self-learning techniques the system will self-adapt to the environment and the hardware on which the system is currently running, with the goal to improve the systems performance

Realtime Object Recognition Using Decision Tree Learning

An object recognition process in general is designed as a domain specific, highly specialized task. As the complexity of such a process tends to be rather inestimable, machine learning is used to achieve better results in recognition. The goal of the process presented in this paper is the computation of the pose of a visible robot, i. e. the distance, angle, and orientation. The recognition process itself, the division into subtasks, as well as the results of the process are presented. The algorithms involved have been implemented and tested on a Sony Aibo