Experience‑Based Learning Systems

Experience‑Based Learning Systems (EBLS) are AI systems that learn from experience, retain it, and use it to solve new problems. They are inspired by human cognitive processes and couple memory and learning with adaptive problem solving. As hybrid AI approaches, they combine symbolic techniques with subsymbolic methods and generative AI. The focus is on:

• Case‑based reasoning (CBR) as the methodological foundation for problem solving by remembering and adapting cases
• Knowledge‑based AI using semantic technologies, ontologies, and planning methods to process and exploit explicit knowledge
• Data‑driven methods such as machine learning and deep learning to analyze existing datasets
• Generative AI and foundation models for analysis, synthesis, and explanation

EBLS act both as autonomous systems and as components of human‑AI teams. They analyze processes, support decisions, adaptively control workflows, and improve through continuous learning from feedback. We study these systems in intelligent process management and in knowledge and experience management in complex application domains.

For prototypical implementations of EBLS we develop modular software frameworks such as ProCAKE and CBRkit, which serve as the technological basis for demonstrating and testing experience‑based approaches.

Our application‑oriented research is conducted in close collaboration with partners from industry and society. EBLS are applied, among others, in the following areas:

Industrial Production

EBLS support planning and decision making in cyber‑physical systems, from process design and real‑time adaptation of production flows through flexible execution to predictive maintenance. We employ experience‑based agentic reasoning, symbolic AI planning, case‑based planning, large language models (LLMs), and reinforcement learning to realize transparent and safe process control.

Water and Energy Management

For resource‑ and energy‑efficient set‑point control we combine robust AI models for forecasting consumption, load, and feed‑in with experience‑based optimization. Digital twins provide the data basis on which the models are continually retrained and adapted in 24/7 operation via continuous learning. EBLS detect anomalies (e.g., leaks), recommend countermeasures, and make decisions with a high degree of automation. They adapt dynamically to changing operating and boundary conditions (e.g., demand, energy prices, weather) to balance cost, CO₂ footprint, and security of supply.

Public Administration & Skilled Trades

EBLS enable intelligent, flexible workflows together with semantic search across heterogeneous document and knowledge bases. Intelligent assistant systems accelerate case handling, increase quality, make decisions traceable, and provide efficient chat‑based interfaces to customers.

Recipe Design

Using case‑based and generative design, recipes including ingredients and processing steps are created or adapted to meet requirements. Existing experiential knowledge is leveraged and combined with deep domain knowledge to develop innovative yet valid recipes.

Emergency and Crisis Management

EBLS semantically integrate heterogeneous situational information from sensors, communication systems, and historical deployment data into a consistent common operating picture. They support control centers in time‑critical situations through explainable action recommendations, forecasts of resource needs, and coordination among responders in dynamic situations.

Healthcare

EBLS analyze treatment processes using process mining, find semantically similar patient cases, and predict next steps for personalized decision support. Guideline knowledge, process data, and explainable models are combined to foster transparency and trust in decisions.