The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.
翻译:在现实环境中,为规划确定高层次知识基础是一项艰巨的任务。这种知识通常是手工制作的,很难不断更新,甚至对于系统专家来说也是如此。最近的方法表明,即使所有中间国家都缺失,传统规划在综合行动模式方面是成功的。这些方法可以将一套执行痕迹中的“规划域定义语言”(PDDL)行动方案(PDDL)综合起来,其中每个执行痕迹至少包括一个初始和最终状态。在本文中,我们建议采用一种新的算法,在行动签名未知时,与一个经典规划者一起不受监督地合成STRIP行动模式。此外,我们还协助汇编了经典规划,以缓解行动模式先决条件中学习静态上游的问题,利用SAT规划者与平行编码的能力来编集行动方案并验证所有实例。我们的系统灵活地支持纳入部分输入信息,从而加快搜索速度。我们通过若干实验展示了如何将学习的行动模式概括于看不见的规划实例。