在PDDL规划中生成实例生成器
Generating Instance Generators in PDDL Planning
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中文总结 AI 辅助
针对PDDL规划中实例生成缺乏规范且手动编写低效的问题，提出利用LLM自动生成实例生成程序，并通过规定检查保证正确性，实验表明能高效生成大量正确且多样的实例。
中文摘要 AI 辅助
PDDL是AI规划社区的事实标准语言，旨在指定规划域：共享相同谓词和动作模式的实例集合。然而，它并未提供任何手段来指定实际的实例集合，即初始状态和目标条件的合法性约束，以及可能指定我们感兴趣的实例子集的域子集约束。其后果之一是实例生成一直是临时性的，需要手动编写针对特定域和子集的实例生成器。近期工作已开始通过推理和学习方法解决此问题，但这些方法存在可扩展性限制。在此，我们引入一种替代方法，利用LLM生成实例生成程序，并通过规定的检查提供内置的正确性保证。我们表明，这些自动生成的实例生成器能够高效地返回大量正确且多样的实例。
英文摘要
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.
发表机构
- German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）
- Saarland University（萨尔兰大学）
- Center for European Research in Trusted Artificial Intelligence (CERTAIN)（欧洲可信人工智能研究中心）
- Ben-Gurion University of the Negev（内盖夫本-古里安大学）
机构由 AI 辅助整理，请以论文原文为准。
