Experience-based Learning Systems

Humans can accomplish a variety of tasks because they have the ability to understand problems, abstract them appropriately, and then combine their knowledge with their experience to develop appropriate solutions. Humans are able to learn from problem solving experience and thus continuously improve themselves, be it by accomplishing tasks better, with fewer mistakes, or faster. 

At the DFKI branch at the University of Trier, we investigate experience-based learning systems in which we try to transfer comparable mechanisms of problem solving and learning to computer systems and implement them in different areas. Experience-based learning systems are hybrid AI systems that use different AI technologies:

• Semantic technologies (ontologies, knowledge graphs)
• Case-Based Reasoning (CBR) & Process-Oriented Case-Based Reasoning (POCBR)
• Machine Learning, incl. Deep Learning
• Planning and Constraint Satisfaction Problem-Solving

Application Area Intelligent Process Management

Increasing digitalization as well as today's requirements regarding the flexibility of production and service provision call for new intelligent approaches to process management using artificial intelligence methods. To this end, we are researching experience-based learning systems with which the redesign, adaptation and optimization of processes is possible, the execution of processes can be supported by flexible workflow systems and the analysis of process data can be performed with regard to the diagnosis of resources and processes.

Application Area Knowledge and Experience Management in the Digital Society

In the modern, digital knowledge society, knowledge and experience must increasingly be networked on a global scale and made available, assessable, and usable. We explore how experience-based learning systems can understand and analyze knowledge and experience in available (mostly textual) forms, and how efficient search and decision support can be realized based on this.