We present an end-to-end framework for planning supported by verifiers. An orchestrator receives a human specification written in natural language and converts it into a PDDL (Planning Domain Definition Language) model, where the domain and problem are iteratively refined by sub-modules (agents) to address common planning requirements, such as time constraints and optimality, as well as ambiguities and contradictions that may exist in the human specification. The validated domain and problem are then passed to an external planning engine to generate a plan. The orchestrator and agents are powered by Large Language Models (LLMs) and require no human intervention at any stage of the process. Finally, a module translates the final plan back into natural language to improve human readability while maintaining the correctness of each step. We demonstrate the flexibility and effectiveness of our framework across various domains and tasks, including the Google NaturalPlan benchmark and PlanBench, as well as planning problems like Blocksworld and the Tower of Hanoi (where LLMs are known to struggle even with small instances). Our framework can be integrated with any PDDL planning engine and validator (such as Fast Downward, LPG, POPF, VAL, and uVAL, which we have tested) and represents a significant step toward end-to-end planning aided by LLMs.

翻译：我们提出了一种由验证器支持的端到端规划框架。编排器接收以自然语言编写的人类规范，并将其转换为PDDL（规划领域定义语言）模型，其中领域与问题通过子模块（智能体）迭代优化，以处理常见的规划需求（如时间约束与最优性）以及人类规范中可能存在的模糊性与矛盾。经验证的领域与问题随后传递给外部规划引擎以生成规划方案。编排器与智能体均由大语言模型驱动，在整个过程中无需人工干预。最后，一个模块将最终规划方案转换回自然语言，以提高人类可读性，同时确保每一步的正确性。我们在多个领域与任务中展示了本框架的灵活性与有效性，包括Google NaturalPlan基准测试与PlanBench，以及Blocksworld和汉诺塔等规划问题（已知大语言模型即使在小规模实例中也难以处理）。本框架可与任何PDDL规划引擎及验证器集成（例如我们已测试的Fast Downward、LPG、POPF、VAL和uVAL），标志着大语言模型辅助的端到端规划迈出了重要一步。