Dillon Z. Chen

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I am a PhD student working in the union of automated planning and machine learning, advised by Sylvie Thiébaux at LAAS-CNRS in the University of Toulouse.

Automated planning refers to sequential decision making problems formally specified by structured and symbolic models. My current interests lie in scaling up automated planning technology through learning knowledge that extrapolates to new unseen problems, and understanding the theoretical capabilities and limitations of such methods. The first half of my PhD has been focused on leveraging graph learning, both deep and classical, for learning to plan. My theoretical and empirical contributions in graph learning for planning are summarised in this workshop paper.

So far I have worked on various delicious flavours of planning, including numeric, multi-objective, stochastic, non-deterministic, and hierarchical planning. I am also familiar with machine learning research, with publications in graph neural networks, foundation models, graph kernels, and applications.

For more research, keep reading below. To play the dog gacha, refresh the page (rates and rarities coming soon!).

News

[2025.07]3 papers accepted to ECAI-25 and workshop papers accepted to TKR@IJCAI-25, ModRef@CP-25, PRL@RLC-25, and EWRL-25.
[2025.02]Most excited to research intern at Vector Institute. Thanks Sheila for the invitation!
[2025.01]140kg bench press achieved.
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Publications

ICAPS, AAAI, NeurIPS and ICLR are A* conferences.

Conference papers

[C8] Dillon Z. Chen and Sylvie Thiébaux. Graph Learning for Numeric Planning. In 38th Conference on Neural Information Processing Systems (NeurIPS), 2024.
[ pdf | poster | slides | code ]
[C7] Dillon Z. Chen and Sylvie Thiébaux. Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning. In 17th International Symposium on Combinatorial Search (SoCS), 2024.
[ pdf | poster | slides | code ]
[C6] Dillon Z. Chen, Felipe Trevizan, and Sylvie Thiébaux. Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning. In 34th International Conference on Automated Planning and Scheduling (ICAPS), 2024.
[ pdf | poster | slides | code | plots ]
[C5] Dillon Z. Chen, Sylvie Thiébaux, and Felipe Trevizan. Learning Domain-Independent Heuristics for Grounded and Lifted Planning. In 38th AAAI Conference on Artificial Intelligence, 2024.
[ pdf | poster | slides | code ]
[C4] Qing Wang, Dillon Z. Chen, Asiri Wijesinghe, Shouheng Li, and Muhammad Farhan. N-WL: A New Hierarchy of Expressivity for Graph Neural Networks. In 11th International Conference on Learning Representations (ICLR), 2023.
[ pdf | poster | slides | code ]
[C3] Dillon Z. Chen, Felipe Trevizan, and Sylvie Thiébaux. Heuristic Search for Multi-Objective Probabilistic Planning. In 37th AAAI Conference on Artificial Intelligence, 2023.
[ pdf | poster | slides | code ]
[C2] Dillon Z. Chen and Pascal Bercher. The Complexity of Flexible FOND HTN Planning. In 32nd International Conference on Automated Planning and Scheduling (ICAPS), 2022. Best Undergraduate Student Paper Award.
[ pdf | poster | slides ]
[C1] Dillon Z. Chen and Pascal Bercher. Fully Observable Nondeterministic HTN Planning – Formalisation and Complexity Results. In 31st International Conference on Automated Planning and Scheduling (ICAPS), 2021. Best Undergraduate Student Paper Award.
[ pdf | poster | slides ]

Workshop papers

[W5] Dillon Z. Chen, Mingyu Hao, Sylvie Thiébaux, and Felipe Trevizan. Graph Learning for Planning: The Story Thus Far and Open Challenges. In 8th Workshop on Generalization in Planning (GenPlan), 2025.
[ pdf | poster | slides ]
[W4] Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava, Michael Katz, and Sylvie Thiébaux. AI Planning: A Primer and Survey (Preliminary Report). In 8th Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL), 2025.
[ pdf | slides ]
[W3] Dillon Z. Chen, Sylvie Thiébaux, and Felipe Trevizan. GOOSE: Learning Domain-Independent Heuristics. In 7th Workshop on Generalization in Planning (GenPlan), 2023. «Subsumed by [C5]».
[ pdf | poster | slides ]
[W2] Dillon Z. Chen, Felipe Trevizan, and Sylvie Thiébaux. Graph Neural Networks and Graph Kernels For Learning Heuristics: Is there a difference?. In 7th Workshop on Generalization in Planning (GenPlan), 2023. «Subsumed by [C6]».
[ pdf | poster ]
[W1] Dillon Z. Chen and Pascal Bercher. The Complexity of Flexible FOND HTN Planning. In 4th ICAPS Workshop on Hierarchical Planning (HPlan), 2021. «Subsumed by [C2]».
[ pdf ]

Theses

[T1] Dillon Z. Chen. GOOSE: Learning Heuristics and Parallelising Search for Grounded and Lifted Planning. Bachelor thesis, The Australian National University, 2023.
[ pdf ]

Reviewing

I have reviewed for the following venues, listed in alphabetical order.

Conferences

Workshops

Miscellaneous Links

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© 2025 Dillon Z. Chen. All rights reserved.