Author name cluster

Daniel Höller

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

39 papers

2 author rows

AAAI Conference 2026 Conference Paper

Learning Heuristic Functions for HTN Planning

Daniel Höller

In recent years, ML-based heuristic functions for automated planning have shown increasing performance. A main challenge is the level of generalization required in planning: techniques must generalize at least across different instances of the same domain (which results in different sizes of learning input). A common approach to overcome the issue is to use graph representations as input. While GNNs are a natural choice for learning, other methods have recently been favored because they show better runtime performance and need less training data. However, existing work has so far been limited to non-hierarchical planning. We describe the first approach to learn heuristics for hierarchical planning. We extend the Instance Learning Graph – a graph structure used in non-hierarchical planning – to the new setting and show how to learn heuristic functions based on it. Since our heuristics are applicable to the lifted model, there is no need to ground it. We therefore combine it with a novel lifted HTN planning system. Like recent systems in non-hierarchical planning, it grounds the search space explored so far, but not the entire model prior to search. Our evaluation shows that our approach is competitive with the lifted systems from the literature, though the ground systems achieve higher coverage.

PDF Details DOI

PRL Workshop 2025 Workshop Paper

Learning Heuristic Functions for HTN Planning

Daniel Höller

During recent years, ML-based heuristic functions for automated planning have shown increasing performance. A main challenge is the level of generalization present in planning: techniques shall generalize at least across different instances of the same domain (which results in different sizes of learning input). To overcome the issue, a common approach is to use graph representations as input. While GNNs are a natural choice for learning, other methods have recently been favored because they show better runtime performance and need less training data. However, the work has been limited to nonhierarchical planning so far. We describe the first approach to learn heuristics for hierarchical planning. We extend the Instance Learning Graph – a graph structure used in nonhierarchical planning – to the new setting and show how to learn heuristic functions based on it. Since our heuristics are applicable to the lifted model, there is no need to ground it, thus we combine it with a novel lifted HTN planning system. Like recent systems in non-hierarchical planning, it grounds the search space explored so far, but not the entire model prior to search. Our evaluation shows that our approach is competitive with the lifted systems from the literature, though the ground systems reach higher coverage.

PDF Details

ECAI Conference 2024 Conference Paper

Decision-Focused Learning to Predict Action Costs for Planning

Jayanta Mandi
Marco Foschini
Daniel Höller
Sylvie Thiébaux
Jörg Hoffmann 0001
Tias Guns

In many automated planning applications, action costs can be hard to specify. An example is the time needed to travel through a certain road segment, which depends on many factors, such as the current weather conditions. A natural way to address this issue is to learn to predict these parameters based on input features (e. g. , weather forecasts) and use the predicted action costs in automated planning afterward. Decision-Focused Learning (DFL) has been successful in learning to predict the parameters of combinatorial optimization problems in a way that optimizes solution quality rather than prediction quality. This approach yields better results than treating prediction and optimization as separate tasks. In this paper, we investigate for the first time the challenges of implementing DFL for automated planning in order to learn to predict the action costs. There are two main challenges to overcome: (1) planning systems are called during gradient descent learning, to solve planning problems with negative action costs, which are not supported in planning. We propose novel methods for gradient computation to avoid this issue. (2) DFL requires repeated planner calls during training, which can limit the scalability of the method. We experiment with different methods approximating the optimal plan as well as an easy-to-implement caching mechanism to speed up the learning process. As the first work that addresses DFL for automated planning, we demonstrate that the proposed gradient computation consistently yields significantly better plans than predictions aimed at minimizing prediction error; and that caching can temper the computation requirements.