This paper studies the problem of action model learning with full observability. Following the learning by search paradigm by Mitchell, we develop a theory for action model learning based on version spaces that interprets the task as search for hypothesis that are consistent with the learning examples. Our theoretical findings are instantiated in an online algorithm that maintains a compact representation of all solutions of the problem. Among these range of solutions, we bring attention to actions models approximating the actual transition system from below (sound models) and from above (complete models). We show how to manipulate the output of our learning algorithm to build deterministic and non-deterministic formulations of the sound and complete models and prove that, given enough examples, both formulations converge into the very same true model. Our experiments reveal their usefulness over a range of planning domains.

翻译：本文研究完全可观测条件下的动作模型学习问题。遵循Mitchell提出的基于搜索的学习范式，我们建立了一种基于版本空间的动作模型学习理论，将任务解释为寻找与学习样本一致的假设。我们的理论成果通过一种在线算法得以实例化，该算法维护了问题所有解的紧凑表示。在这些解的范围中，我们关注从下方（健全模型）和从上方（完整模型）逼近实际转移系统的动作模型。我们展示了如何操作学习算法的输出，以构建健全模型和完整模型的确定性与非确定性形式，并证明在给定足够样本的情况下，这两种形式都会收敛到相同的真实模型。我们的实验揭示了这些模型在一系列规划领域中的实用性。