从动作轨迹中自动学习完整的STRIPS模型，无需先验知识。
Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution
- 通过新测试判断谓词是否受特定动作模式影响，实现高效推理。
- 在8-谜题等经典场景中处理超十万状态，学习结果可验证。
- 适用于任意领域，无需限制谓词数量或类型，适合自动化规划研究者。
仅从动作轨迹中学习STRIPS动作模型是一项挑战性任务，因为需要同时学习领域谓词。本文提出一种新方法，兼具LOCM系统的可扩展性与SAT方法的完备性与正确性。该方法不依赖对隐藏领域或谓词数量、变元数的限制，基于一个高效的新测试，检验某谓词是否受一组动作模式（即特定参数位置的动作）影响，其结果一致则视为有效。通过该测试筛选出的谓词与动作模式构成学习基础，进而轻松补充前提条件和静态谓词。理论与实验双重验证表明，该方法在标准经典域如8-谜题上表现优异，处理涉及数十万状态与转移的轨迹数据，并在更大实例上验证了学习表示的有效性。
原文摘要 · Abstract (English)
Learning STRIPS action models from action traces alone is a challenging problem as it involves learning the domain predicates as well. In this work, a novel approach is introduced which, like the well-known LOCM systems, is scalable, but like SAT approaches, is sound and complete. Furthermore, the approach is general and imposes no restrictions on the hidden domain or the number or arity of the predicates. The new learning method is based on an \emph{efficient, novel test} that checks whether the assumption that a predicate is affected by a set of action patterns, namely, actions with specific argument positions, is consistent with the traces. The predicates and action patterns that pass the test provide the basis for the learned domain that is then easily completed with preconditions and static predicates. The new method is studied theoretically and experimentally. For the latter, the method is evaluated on traces and graphs obtained from standard classical domains like the 8-puzzle, which involve hundreds of thousands of states and transitions. The learned representations are then verified on larger instances.
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