6 citations · 11 across the 14 of their papers we have counts for
14 papers
Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen +1
We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning from demonstrations has shown itself to be effecti…
Satisficing and Optimal Generalised Planning via Goal Regression (Extended Version)
Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen +1
Generalised planning (GP) refers to the task of synthesising programs that solve families of related planning problems. We introduce a novel, yet simple method for GP: given a set…
Symmetry-Invariant Novelty Heuristics via Unsupervised Weisfeiler-Leman Features
Dillon Z. Chen
Novelty heuristics aid heuristic search by exploring states that exhibit novel atoms. However, novelty heuristics are not symmetry invariant and hence may sometimes lead to redunda…
Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study
Dillon Z. Chen
Weisfeiler-Leman Features (WLFs) are a recently introduced classical machine learning tool for learning to plan and search. They have been shown to be both theoretically and empiri…
Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies
Dillon Z. Chen, Johannes Zenn, Tristan Cinquin +1
We study the usage of language models (LMs) for planning over world models specified in the Planning Domain Definition Language (PDDL). We prompt LMs to generate Python programs th…
Relational GNNs Cannot Learn Features for Planning
Dillon Z. Chen
Relational Graph Neural Networks (R-GNNs) are a GNN-based approach for learning value functions that can generalise to unseen problems from a given planning domain. R-GNNs were the…
AI Planning: A Primer and Survey (Preliminary Report)
Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava +2
Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations researc…
Graph Learning for Planning: The Story Thus Far and Open Challenges
Dillon Z. Chen, Mingyu Hao, Sylvie Thiébaux +1
Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instance…
WLPlan: Relational Features for Symbolic Planning
Dillon Z. Chen
Scalable learning for planning research generally involves juggling between different programming languages for handling learning and planning modules effectively. Interpreted lang…
Deep Learning for Generalised Planning with Background Knowledge
Dillon Z. Chen, Rostislav Horčík, Gustav Šír
Automated planning is a form of declarative problem solving which has recently drawn attention from the machine learning (ML) community. ML has been applied to planning either as a…
Graph Learning for Numeric Planning
Dillon Z. Chen, Sylvie Thiébaux
Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take a…
Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning
Dillon Z. Chen, Sylvie Thiébaux
Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric…
Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning
Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux
Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this w…
Learning Domain-Independent Heuristics for Grounded and Lifted Planning
Dillon Z. Chen, Sylvie Thiébaux, Felipe Trevizan
We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular…