Fakultätspreise der KIT-Fakultät für Wirtschaftswissenschaften

P. Deininger
Patrick Deininger

Preisträgerinnen und Preisträger 2021

Patrick Deininger: Monte Carlo Tree Search for Production Scheduling in Matrix Production

Über die Masterarbeit: One challenge of production scheduling for matrix productions is the near real time generation of good machine schedules, a NP-hard problem. Patrick Deininger has developed a central, holistic decision instance, which is based on multi-objective Monte Carlo Tree Search (MCTS) and capable of finding pareto solutions with respect to individual preferences in near real-time. The thesis considers two modifications of MCTS. (1) The dynamic adaption of exploration and exploitation improves local optima. (2) The learning of promising combination rules for action generation facilitates adaptability to changing optimization contexts and improves the generalizability, search speed and solution quality significantly.

1-Minütiges Erklärvideo über die Masterarbeit

Portrait P. Hemmer
Patrick Hemmer

Patrick Hemmer: DEAL: Deep Evidential Active Learning for Image Classification

Über die Masterarbeit: Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks. However, large labeled data sets are generally needed for model training. Unlabeled data is available in many domains, but labeling is expensive, e.g., when specific expert knowledge is required. In this thesis, a novel Active Learning algorithm that learns from unlabeled data by capturing high prediction uncertainty is proposed to mitigate the problem of extensive data labeling. We demonstrate with publicly available data and in the context of a quality assurance industry use case that our approach leads to a significant reduction in the number of labeled images necessary to achieve a predefined performance contributing to a considerable decrease in labeling overhead.

1-Minütiges Erklärvideo über die Masterarbeit

J.L. Zhang
Jingyi Lisa Zhang

Jingyi Lisa Zhang:Overcoming the Cold Start Problem in AI Technology - A Survey-based Approach on Example of Wearable Sleep Tracker AURA

Über die Masterarbeit: Wearable sleep technology such as AURA represents a novel technology that promises to deliver the most accurate sleep measurement as well as sleeping improvement suggestions and thus generates benefits for customers in need. However, there is a critical time period between users’ first interaction and the first valid piece of sleep-improving suggestion the algorithm can generate due to development of statistical significance which hence, leads to unclear acceptance of customers. After relevant case study, this work aims to explore various value perception and expectation of possible segments in terms of their preferences in sleep wearable area, in particular regarding the critical initial phase (“cold start”). First, the experimental study reveals users’ implicit valuations via conjoint analysis. Attributes of Design, measured sleep parameter and frequency of necessary input are the most important in a nutshell. The study also provides empirical evidence for the existence of three dissimilar clusters, that is, design preferred lifestylers, indifferent average followers and detail-oriented functional desirers. The findings imply different strategies of recommended course of action regarding the relevance of the cold start problem. Finally, implications are discussed and future research options are sketched out.