Presents efficient holistic lookahead encoding and abstracted IW(1) that enable relational GNN policies to achieve new SOTA results surpassing prior work and LAMA on hyperscaling IPC 2023 benchmarks.
The 2023 International Planning Competition.AI Magazine, 45(2):280–296
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Property-guided LLM program synthesis with counterexample feedback creates direct heuristics for PDDL planning domains that require far fewer generations and less evaluation cost than score-based baselines.
citing papers explorer
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Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
Presents efficient holistic lookahead encoding and abstracted IW(1) that enable relational GNN policies to achieve new SOTA results surpassing prior work and LAMA on hyperscaling IPC 2023 benchmarks.
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Property-Guided LLM Program Synthesis for Planning
Property-guided LLM program synthesis with counterexample feedback creates direct heuristics for PDDL planning domains that require far fewer generations and less evaluation cost than score-based baselines.