用逻辑公式学习可解释的智能体行为模型，适用于PDDL规划任务。
Learning Interpretable Classifiers for PDDL Planning
- 通过小样本学习一阶时序逻辑公式描述智能体行为。
- 在合理时间内生成准确且可读的分类公式，泛化到未见实例。
- 适合关注可解释性与智能体行为分析的研究者。
我们研究如何合成可解释的模型，以识别智能体在一组相似的PDDL规划任务中相对于其他智能体的行为特征。方法是从少量展示智能体解决小型规划实例的例子中学习逻辑公式，这些公式基于专为规划形式化设计的一阶时序逻辑（FTL）表达。所学公式具有人类可读性，可作为智能体策略的部分描述，并能推广至未见实例。我们证明该学习问题是NP难的。为此，提出一种拓扑引导的MaxSAT编译方法，可生成多样化的公式。实验表明，可在合理时间内学习到既有趣又准确的公式。
原文摘要 · Abstract (English)
We consider the problem of synthesizing interpretable models that recognize the behaviour of an agent compared to other agents, on a whole set of similar planning tasks expressed in PDDL. Our approach consists in learning logical formulas, from a small set of examples that show how an agent solved small planning instances. These formulas are expressed in a version of First-Order Temporal Logic (FTL) tailored to our planning formalism. Such formulas are human-readable, serve as (partial) descriptions of an agent's policy, and generalize to unseen instances. We show that learning such formulas is computationally intractable, as it is an NP-hard problem. As such, we propose to learn these behaviour classifiers through a topology-guided compilation to MaxSAT, which allows us to generate a wide range of different formulas. Experiments show that interesting and accurate formulas can be learned in reasonable time.
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