Search Results for author:
Found 3 papers, 0 papers with code
Learning Visual Planning Models from Partially Observed Images
Although a number of approaches have been developed for learning planning models from fully observed unstructured data (e. g., images), in many scenarios raw observations are often incomplete.
Refining HTN Methods via Task Insertion with Preferences
Hierarchical Task Network (HTN) planning is showing its power in real-world planning.
Representation Learning for Classical Planning from Partially Observed Traces
The experimental results show that the domain models learned by our approach are much more effective on solving real planning problems.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
