TL;DR
It is shown a proof-of-the-concept lifted planner can sometimes solve the BPP problem by using a domain-independent heuristic that guides search for a plan.
This work introduces a semantic-preserving PDDL-to-Lean conversion, and uses an LLM to generate both the generalized plan and the formal proof that it solves every instance satisfying the domain constraints, and evaluates this approach on 13 commonly used benchmark domains.
This paper introduces Conditional TPOs (cTPOs), which extend TPOs with richer relative-timing constraints and conditional event activations based on environmental conditions and proves that this decomposition is complete and preserves plan optimality while improving the interpretability of complex tasks.
Language agents solve complex tasks through plans and actions. A single step the world refuses puts the goal out of reach, and what the agent does next decides the task. Prompted planners fail at exactly this point, rewriting the refused step in new words, meeting the same refusal, and burning the attempt budget withou...
It is argued that domain abstractions offer a framework that lends itself much better to simple numeric planning, with abstract state spaces that are computed incrementally using counterexample-guided abstraction refinement (CEGAR), avoiding the exhaustive exploration of PDBs.
A framework to exploit task-level contracts, expressed as assumptions and guarantees at the beginning and end of tasks, to automatically synthesize plans that are guaranteed to be correct on any system satisfying the contracts is proposed.
Improvements show that an explicit graph world model harness can substantially improve the reliability and efficiency of long-horizon embodied planning across compact and frontier hosted LLM capabilities.
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