Hybrid Reasoning for Intelligent Systems
Knowledge Representation and Reasoning (KR&R) and, in particular, reasoning about actions, their effects and the environment in which actions take place, is fundamental for intelligent behaviour and has been a central concern in Artificial Intelligence from the beginning. To date, most KR&R approaches have focused on qualitative representations, which contrasts with requirements from many application domains where quantitative information needs to be processed as well. Examples of such quantitative aspects are time, probabilistic uncertainty, multi-criteria optimization, or resources like mass.
The aim of this research unit is to integrate both qualitative and quantitative forms of reasoning, resulting in hybrid reasoning formalisms. The work will build on the results from the previous DFG-funded project cluster "Logic-Based Knowledge Representation", where some of the most successful qualitative KR&R formalisms were combined with a focus on reasoning about actions. To increase the practical impact and relevance of the proposed research, experts from two carefully selected application areas, Robotics and Bioinformatics, will be part of the consortium, and both foundational and application-driven projects will be carried out. In the long run, the research unit will take a big step towards widening the use of KR&R technology as part of large, complex intelligent systems.