arXiv:2501. 18784v5 Announce Type: replace Abstract: Heuristics are a central component of deterministic planning, particularly in domain-independent settings where general applicability is prioritized over task-specific tuning.
arXiv:2606. 02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning.
arXiv:2605. 29649v2 Announce Type: replace Abstract: Heuristic search is the dominant paradigm in symbolic AI planning, and the strongest heuristics are the result of decades of work by planning researchers.
arXiv:2605. 28566v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, yet their standard generation process -- auto-regressive token prediction -- is inherently myopic and prone to cascading errors.
Automatic Heuristic Design (AHD) has emerged as a transformative approach for solving combinatorial optimization problems. While recent Large Language Model (LLM)-based methods have shown promise, they predominantly rely on fixed evolutionary operators and struggle to effectively accumulate and reuse historical search experience.
PIE-APT introduces a unified framework for abductive planning over Temporal Dynamic Knowledge Graphs (TDKGs) using two modules: PIE-Abducer, which performs incremental direct-derivation abduction, and PIE-APT, which interleaves backward‑chaining A* search with PIE-Abducer to generate action sequences and abductive assumptions. The approach operates natively on the expressive SROIQ Description Logic, leveraging an incremental reasoner to maintain decidability and bypass the Ramification Problem. Evaluation on four OWL benchmarks demonstrates qualitative superiority over classical planners and shows that the direct‑derivation method outperforms a Minimal Hitting Set baseline in abductive enrichment.
arXiv:2608. 16637v1 Announce Type: new Abstract: LLMs remain unreliable for long-horizon planning, often generating logically inconsistent or non-applicable plans.
arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.
arXiv:2606. 15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it.
MAPLE is a new agent that maintains and updates optimization problems through successive natural‑language requests, combining language‑based problem construction with mathematical programming and evolutionary search. It preserves the optimization program, accepted plans, earlier updates, and candidate solutions for future requests, enabling rapid adaptation to changing business constraints. In a benchmark of 15 trajectories and 180 updates across various operational domains, MAPLE completed all trajectories with high online scalar quality and Pareto hypervolume ratio, and maintained update validity and useful search information across substantial revisions.
arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.
arXiv:2609.06071v1 Announce Type: new Abstract: PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predi...
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.