Klaus Häming

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We discuss the use of Ordinal Conditional Functions (OCF) in the context of Reinforcement Learning while introducing a new revision operator for conditional information. The proposed method is compared to the state-of-the-art method in a small Reinforcement Learning application with added futile information, where generalization proves to be advantageous.
The problem addressed in this paper is the reconstruction of an object in the form of a realistically textured 3D model from images taken with an uncalibrated camera. We especially focus on reconstructions from short image sequences. By means of a description of an easy to use system, which is able to accomplish this in a fast and reliable way, we give a(More)
This paper addresses the topic of image rectification, a widely used technique in 3D-reconstruction and stereo vision. The most popular algorithm uses a projective transformation to map the epipoles of the images to infinity. This algorithm fails whenever an epipole lies inside an image. To overcome this drawback, a rectification scheme known as polar(More)
To enable a reinforcement learning agent to acquire symbolical knowledge, we augment it with a high-level knowledge representation. This representation consists of ordinal conditional functions (OCF) which allow it to rank world models. By this means the agent is enabled to complement the self-organizing capabilities of the low-level reinforcement learning(More)
We introduce a tool which allows an untrained user to take three images of an object freehand with a simple consumer camera. From these images a 3d model of the visible parts of the object is reconstructed within seconds and visualized realistically. From a research point of view we propose solutions for three weaknesses of the state-of-the-art(More)
We propose a combination of belief revision and reinforcement learning which leads to a self-learning agent. The agent shows six qualities we deem necessary for a successful and adaptive learner. This is achieved by representing the agent’s belief in two different levels, one numerical and one symbolical. While the former is implemented using basic(More)