Latest Projects
Research project (§ 26 & § 27)
Duration
: 2024-03-01 - 2028-05-01
In this project, novel scenarios are tested and evaluated, and work is done on how to bring "common sense" into robots. The approach followed is "human-in-the-loop", combining the advantages of human experts with the advantages of AI.
Specifically, this project tests and evaluates deployment scenarios of robots in forestry technology and presents new deployment scenarios. The novelty of this approach allows us to explore basics in terms of requirements, challenges and future possibilities in dealing with such systems, thus paving the way for more advanced basic projects or applications. Specifically, this project is expected to result in a series of international publications and an infrastructure
to research and test the fundamentals for the use of future AI technologies and to apply them in teaching. Finally, the emerging robot test park in Tulln – adjacent to the new House of Digitalization - may also generate broad interest in the topic. The research methodology follows a 3G pioneer research approach with agile human-centered design: generation 1 testing of existing technology, generation 2 adaptation of existing technology with low-cost means, generation 3 advanced adaptation that goes beyond the state of the art and is planned together with our partners in Canada and UK - world leading robotics institutes.
The infrastructure funded by this proposal will serve existing projects and is intended to spur new, larger projects (e.g.,EU). Added values are planned on three levels: 1) for the international AI research community through publications, 2) for the state of Lower Austria through a) later practical usage possibilities, and b) as an important contribution to teaching and making AI education more attractive to young researchers to counteract the labor shortage in AI.
Research project (§ 26 & § 27)
Duration
: 2023-11-15 - 2026-01-14
In the last 20 years, numerous road maintenance devices have entered the market. The easily operable hydraulic attachments for tractors promise efficient and cost-effective maintenance of forest roads. Depending on the road maintenance device, four to five maintenance passes are required between April and September to uphold the durability, driving comfort, and traffic safety of forest roads. Consistent use can significantly extend the interval between major repairs. There are compelling reasons to maintain forest roads using these road maintenance devices, but concerns arise regarding the technical functionality of forest roads. Lack of experience among equipment operators or inconsistent use can negatively impact the road surface. Reasons for this include the entry of unwanted materials into the road structure or the tearing of the usually combined surface-support lay‐ers, leading to compaction problems. After the use of a road maintenance device, forest owner are often confronted with com‐plaints from recreation seekers in the forest. The consideration of whether a forest road is suitable for such maintenance activi‐ties, or how a forest road should be restored to ensure the suitability of the road maintenance devices, are the goals of this project.
Research project (§ 26 & § 27)
Duration
: 2022-05-01 - 2024-11-03
The progress of statistical machine learning methods has made AI increasingly successful. Deep learning exceeds human performance even in the medical domain. However, their full potential is limited by their difficulty to generate underlying explanatory structures, hence they lack an explicit declarative knowledge representation. A motivation for this project are rising legal and privacy issues – to understand and retrace machine decision processes. Transparent algorithms could appropriately enhance trust of medical professionals, thereby raising acceptance AI solutions generally. This project will provide important contributions to the international research community in the following ways: 1) evidence in various methods of explainability, patterns of explainability, and explainability measurements. Based on empirical studies (“How do humans explain ?”) we will develop a library of explanatory patterns and a novel grammar how these can be combined. Finally, we will define criteria/benchmarks for explainability and provide answers to the question “What is a good explanation?”. 2) Principles to measure effectiveness of explainability and explainability guidelines and 3) Mapping human understanding with machine explanations and deploying an open explanatory framework along with a set of benchmarks and open data to stimulate and inspire further research among the international AI/machine learning community. All outcomes of this project will be made openly available to the international research community.