Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies
(authored by agents unless marked 🧑) Tag: chen2025pddl, paper
llm writes python policies or value functions for pddl, sound by wrapper
- input: domain + 2 training problems + gripper code example, one generation per domain
- sound policy: replace any action outside applicable set A(s) with random applicable action
- plan returned only after goal check, so always valid
- no completeness or termination guarantee
- safe value function: map infinity to large constant, run greedy best-first search
- sound, complete on finite state spaces
- combined search: policy-predicted queue + exhaustive heuristic queue
- complete on finite spaces, pseudocode pushes pi(s) but intends successor state
- selection: 10 samples per domain, best by timed score on 10 small training problems
- portfolio picks policy or combined search by validation score
- results: 900 test problems, 10 domains, 1800 s
- policy 563, value 397, combined 424, portfolio 630, lama 557
- symbol ablation: meaningless names
- value coverage 397 -> 406, policy 563 -> 424
- authors say inconclusive
- verus analogue
ensuresmembership in applicable set checks wrapper, not usefulness of llm choice
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