Previous STRIPS domain model acquisition approaches that learn from state traces start with the names and parameters of the actions to be learned. Therefore their only task is to deduce the preconditions and effects of the given actions. In this work, we explore learning in situations when the parameters of learned actions are not provided. We define two levels of trace quality based on which information is provided and present an algorithm for each. In one level (L1), the states in the traces are labeled with action names, so we can deduce the number and names of the actions, but we still need to work out the number and types of parameters. In the other level (L2), the states are additionally labeled with objects that constitute the parameters of the corresponding grounded actions. Here we still need to deduce the types of the parameters in the learned actions. We experimentally evaluate the proposed algorithms and compare them with the state-of-the-art learning tool FAMA on a large collection of IPC benchmarks. The evaluation shows that our new algorithms are faster, can handle larger inputs and provide better results in terms of learning action models more similar to reference models.

翻译：以往的基于状态迹学习的STRIPS领域模型获取方法，需要预先知道所学动作的名称和参数。因此，它们的唯一任务是推导给定动作的前提条件和效果。在本研究中，我们探索了在未提供所学动作参数情况下的学习问题。我们根据所提供的信息定义了两种状态迹质量等级，并为每种等级提出了一种算法。在等级一（L1）中，状态迹中的状态标有动作名称，因此我们可以推导出动作的数量和名称，但仍需确定参数的数量和类型。在等级二（L2）中，状态额外标有构成对应基化动作参数的对象，此时我们仍需推导所学动作中参数的类型。我们通过实验评估了所提出的算法，并在大量IPC基准测试上将它们与先进的学习工具FAMA进行了比较。评估结果表明，我们的新算法速度更快、能处理更大规模的输入，并且在学习更接近参考模型的动作模型方面取得了更好的结果。