arXiv:2607. 21414v1 Announce Type: new Abstract: In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula.
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The paper presents a method for automatically generating generalized plans in Lean, along with formal proofs of their completeness for given domain constraints. It introduces a semantic‑preserving conversion from PDDL to Lean and uses an LLM to produce both the plan and its proof, whose correctness is verified by Lean’s kernel. Evaluated on 13 benchmark domains with GPT‑5.6‑Sol, the approach yields complete plans and valid proofs for 12 of them, marking a significant advance in automated generalized‑plan completeness.
arXiv:2403. 19883v2 Announce Type: replace Abstract: Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty.
arXiv:2606. 14574v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments.
The paper introduces OHCAM, an online method for learning action models that include conditional and quantified effects from limited interactions. It maintains a belief over possible models and actively chooses actions that maximize disagreement among hypotheses to reduce uncertainty, while handling noisy observations. Starting with simple hypotheses, OHCAM expands complexity only when necessary, achieving sample‑efficient learning that outperforms baselines on benchmark domains and is validated on a Kinova Gen3 robot.
arXiv:2602.00276v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning...
arXiv:2506. 09171v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly capable, but LLM agents still struggle to plan effectively in interactive, partially observable, long-horizon environments when search is unguided or recent history is insufficient.