学习行动模式：质的方法
Learning Action Models: Qualitative Approach
摘要：
在动态认知逻辑中，动作使用动作模型来描述。在本文中，我们介绍了一个框架，从观察研究行动模型的可学习性。我们目前的第一个结果有关命题的行动模式。首先，我们检查两个基本的可学习性标准：有限可识别性（在有限时间内最终推断出适当的动作模型）和极限可识别性（不确定收敛到正确的动作模型）。我们发现，确定性的行动是可识别的，而非确定性的行动需要更多的学习能力，他们是可识别的限制。然后，我们转向一种特定的学习方法，该方法通过限制特定于学习的动作模型中的事件空间来进行。这种学习方式非常类似于动态认知逻辑中众所周知的更新方法。我们介绍了几种不同的学习方法适合有限的可识别性的特定类型的确定性行动。
英文摘要：
In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power-they are identifiable in the limit. We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning methods suited for finite identifiability of particular types of deterministic actions.
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