http://dl.gi.de/handle/20.500.12116/36652 2026-07-23T12:11:49Z 2026-07-23T12:11:49Z Steen, Alexander http://dl.gi.de/handle/20.500.12116/36658 2021-06-21T12:23:08Z 2019-01-01T00:00:00Z

Higher-order theorem proving and its applications Steen, Alexander Automated theorem proving systems validate or refute whether a conjecture is a logical consequence of a given set of assumptions. Higher-order provers have been successfully applied in academic and industrial applications, such as planning, software and hardware verification, or knowledge-based systems. Recent studies moreover suggest that automation of higher-order logic, in particular, yields effective means for reasoning within expressive non-classical logics, enabling a whole new range of applications, including computer-assisted formal analysis of arguments in metaphysics. My work focuses on the theoretical foundations, effective implementation and practical application of higher-order theorem proving systems. This article briefly introduces higher-order reasoning in general and presents an overview of the design and implementation of the higher-order theorem prover Leo-III. In the second part, some example applications of Leo-III are discussed.

2019-01-01T00:00:00Z Wanka, Rolf http://dl.gi.de/handle/20.500.12116/36654 2021-06-21T12:23:08Z 2019-01-01T00:00:00Z

Swarm intelligence Wanka, Rolf Article Swarm intelligence was published on August 1, 2019 in the journal it - Information Technology (volume 61, issue 4).

2019-01-01T00:00:00Z Weißgerber, Thomas Granitzer, Michael http://dl.gi.de/handle/20.500.12116/36660 2021-06-21T12:23:08Z 2019-01-01T00:00:00Z

Mapping platforms into a new open science model for machine learning Weißgerber, Thomas; Granitzer, Michael Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.

2019-01-01T00:00:00Z Vogelsang, Andreas http://dl.gi.de/handle/20.500.12116/36659 2021-06-21T12:23:08Z 2019-01-01T00:00:00Z

Explainable software systems Vogelsang, Andreas Software and software-controlled technical systems play an increasing role in our daily lives. In cyber-physical systems, which connect the physical and the digital world, software does not only influence how we perceive and interact with our environment but software also makes decisions that influence our behavior. Therefore, the ability of software systems to explain their behavior and decisions will become an important property that will be crucial for their acceptance in our society. We call software systems with this ability explainable software systems . In the past, we have worked on methods and tools to design explainable software systems. In this article, we highlight some of our work on how to design explainable software systems. More specifically, we describe an architectural framework for designing self-explainable software systems, which is based on the MAPE-loop for self-adaptive systems. Afterward, we show that explainability is also important for tools that are used by engineers during the development of software systems. We show examples from the area of requirements engineering where we use techniques from natural language processing and neural networks to help engineers comprehend the complex information structures embedded in system requirements.

2019-01-01T00:00:00Z