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Jendrik Seipp

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53 papers

2 author rows

AAAI Conference 2026 Conference Paper

Symmetry-Aware Transformer Training for Automated Planning

Markus Fritzsche
Elliot Gestrin
Jendrik Seipp

While transformers excel in many settings, their application in the field of automated planning is limited. Prior work like PlanGPT, a state-of-the-art decoder-only transformer, struggles with extrapolation from easy to hard planning problems. This in turn stems from problem symmetries: planning tasks can be represented with arbitrary variable names that carry no meaning beyond being identifiers. This causes a combinatorial explosion of equivalent representations that pure transformers cannot efficiently learn from. We propose a novel contrastive learning objective to make transformers symmetry-aware and thereby compensate for their lack of inductive bias. Combining this with architectural improvements, we show that transformers can be efficiently trained for either plan-generation or heuristic-prediction. Our results across multiple planning domains demonstrate that our symmetry-aware training effectively and efficiently addresses the limitations of PlanGPT.

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IJCAI Conference 2025 Conference Paper

Abstraction Heuristics for Classical Planning Tasks with Conditional Effects

Martín Pozo
Jendrik Seipp

In planning tasks, conditional effects model action outcomes that depend on the current state of the world. Conditional effects are a crucial modeling feature since compiling them away can cause an exponential growth in task size. However, only a few admissible heuristics support them. To add abstraction heuristics to this set, we show how to compute projections, Cartesian abstractions and merge-and-shrink abstractions for tasks with conditional effects. Our experiments show that these heuristics are competitive with, and often surpass, the state-of-the-art for conditional-effect tasks.

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NeurIPS Conference 2025 Conference Paper

Classical Planning with LLM-Generated Heuristics: Challenging the State of the Art with Python Code

Augusto B. Corrêa
André G. Pereira
Jendrik Seipp

In recent years, large language models (LLMs) have shown remarkable performance in many problems. However, they fail to plan reliably. Specialized attempts to improve their planning capabilities still produce incorrect plans and fail to generalize to larger tasks. Furthermore, LLMs designed for explicit "reasoning" fail to compete with automated planners while increasing computational costs, which reduces one of the advantages of using LLMs. In this paper, we show how to use LLMs to always generate correct plans, even for out-of-distribution tasks of increasing size. For a given planning domain, we ask an LLM to generate several domain-dependent heuristic functions in the form of Python code, evaluate them on a set of training tasks with a greedy best-first search, and choose the best one. The resulting LLM-generated heuristic functions solve substantially more unseen out-of-distribution test tasks than end-to-end LLM planning, particularly for non-reasoning LLMs. Moreover, they also solve many more tasks than state-of-the-art domain-independent heuristics for classical planning, and are competitive with the strongest learning algorithm for domain-dependent planning. These results are impressive given that our implementation is based on a Python planner and the baselines all build upon highly optimized C++ code. In some domains, the LLM-generated heuristics expand fewer states than the baselines, showing that they are not only efficiently computable but also more informative than the state-of-the-art heuristics. Overall, our results show that sampling a set of planning heuristic functions can significantly improve the planning capabilities of LLMs.

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ECAI Conference 2025 Conference Paper

Combining Heuristics and Transition Classifiers in Classical Planning

Farid Musayev
Dominik Drexler
Daniel Gnad 0001
Jendrik Seipp

Recent work on learning for classical planning has primarily focused on exclusively employing the learned heuristics or policies. However, no purely learning-based method has consistently outperformed state-of-the-art planners to date. To address this, we return to the research paradigm that integrates learned domain knowledge with traditional, non-learned planning techniques. We propose a novel and simple approach for learning transition classifiers, using tree-based statistical learning over description logic features. In experiments, we evaluate various strategies for integrating learned classifiers with the FF heuristic, a state-of-the-art non-learned heuristic. Our results demonstrate that augmenting classical heuristics with transition classifiers leads to substantial performance improvements. The strongest variant combines classifier-based lookahead search with learned knowledge to avoid transitions into unsolvable states, frequently outperforming state-of-the-art traditional and learning-based planners.