用大模型自动生成符合规范的规划实例生成器。
Generating Instance Generators in PDDL Planning
- 利用大模型生成实例生成程序，内置校验保证正确性。
- 可高效产出大量合规且多样化的规划实例。
- 适合需要大规模测试用例的规划系统研究者。
PDDL作为人工智能规划领域的事实标准语言，用于定义规划领域：共享相同谓词和动作模式的一组实例。然而，它并未提供指定实际实例集的方法，即初始状态与目标条件的合法性约束，以及可能存在的领域子集约束。这导致实例生成长期依赖人工编写、针对特定领域和子集的生成器。近期工作尝试通过推理和学习方法解决此问题，但面临可扩展性限制。本文提出一种新方法，利用大模型生成实例生成程序，并通过预设检查实现内在正确性保障。实验表明，这些自动生成的实例生成器能高效返回大量合规且多样的实例。
原文摘要 · Abstract (English)
PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that share the same predicates and action schemas. Yet it does not provide any means to specify the actual instance set, i.e., legality constraints on initial states and goal conditions, as well as possibly domain subset constraints specifying an instance subset we are interested in. One consequence of this is that instance generation has always been ad-hoc, with manually written domain- and subset-specific instance generators. Recent work has started to address this, through reasoning and learning methods that however suffer from scalability limitations. Here we introduce an alternative approach, leveraging LLMs to generate instance-generation programs, with built-in soundness guarantees through prescribed checks. We show that these automatically generated instance generators return large numbers of sound and diverse instances efficiently.
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