1 citations · 1 across the 6 of their papers we have counts for
6 papers
Generating Instance Generators in PDDL Planning
Nicola J. Müller, Naya Rudolph, Katharina Stein +3
PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predicates and action schemas. Yet…
Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance
Julius Gabelmann, Felix Jahn, Kevin Baum +4
The widespread adoption of AI chatbots in education will drastically change learning, making responsible deployment a critical concern. While large language models (LLMs) might hav…
Per-Domain Generalizing Policies: On Learning Efficient and Robust Q-Value Functions (Extended Version with Technical Appendix)
Nicola J. Müller, Moritz Oster, Isabel Valera +2
Learning per-domain generalizing policies is a key challenge in learning for planning. Standard approaches learn state-value functions represented as graph neural networks using su…
Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior
Timo P. Gros, Nicola J. Müller, Daniel Fiser +3
Recent work has shown that successful per-domain generalizing action policies can be learned. Scaling behavior, from small training instances to large test instances, is the key ob…
Acting for the Right Reasons: Creating Reason-Sensitive Artificial Moral Agents
Kevin Baum, Lisa Dargasz, Felix Jahn +2
We propose an extension of the reinforcement learning architecture that enables moral decision-making of reinforcement learning agents based on normative reasons. Central to this a…
Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Version
Timo P. Gros, Daniel Höller, Jörg Hoffmann +1
Learning-based approaches for solving large sequential decision making problems have become popular in recent years. The resulting agents perform differently and their characterist…