4 papers · 1 filter
Attribution-based Explanations for Markov Decision Processes
Paul Kobialka, Andrea Pferscher, Francesco Leofante +3
Attribution techniques explain the outcome of an AI model by assigning a numerical score to its inputs. So far, these techniques have mainly focused on attributing importance to st…
Counterfactual Strategies for Markov Decision Processes
Paul Kobialka, Lina Gerlach, Francesco Leofante +3
Counterfactuals are widely used in AI to explain how minimal changes to a model's input can lead to a different output. However, established methods for computing counterfactuals t…
BedreFlyt: Improving Patient Flows through Hospital Wards with Digital Twins
Riccardo Sieve, Paul Kobialka, Laura Slaughter +3
Digital twins are emerging as a valuable tool for short-term decision-making as well as for long-term strategic planning across numerous domains, including process industry, energy…
ROSA: A Knowledge-based Solution for Robot Self-Adaptation
Gustavo Rezende Silva, Juliane PäÃler, S. Lizeth Tapia Tarifa +2
Autonomous robots must operate in diverse environments and handle multiple tasks despite uncertainties. This creates challenges in designing software architectures and task decisio…