中文摘要：

派生谓词是描述动作非直接效果的主要方式．但是由人类专家设计的派生谓词规则（即领域理论）不能保证总是正确或者完备的，因此有时很难解释一个观察到的规划解为什么是有效的．结合归纳学习与分析学习的优点，文中提出一种称为FODRL（First-Order Derived RulesLearning）的算法，在不完美的初始领域理论的引导下从观察到的规划解中学习一阶派生谓词规则．FODRL基于归纳学习算法FOIL（First-Order Inductive Learning），最主要的改进是可以使用派生谓词的激活集来扩大搜索步，从而提高学习到的规则的精确度．学习过程分为两个步骤：先从规划解中提取训练例，然后学习能够最好拟合训练例和初始领域理论的一阶规则集．在PSR和PROME—LA两个派生规划领域进行实验，结果表明，在大部分情况下FODRL比FOIL（甚至包括其变型算法FOCL）学习到的规则的精确度都要高．

英文摘要：

Derived predicates are a natural way to depict indirect effects of domain actions, and their truth values in the current state are inferred from that of other predicates via domain rules. However, domain rules designed by human experts cannot be guaranteed to be correct or com- plete. So it is often difficult to explain why an observed plan is valid under imperfect domain rules. Combining inductive learning with analytical learning, in this paper, we develop an algo- rithm called FODRL （First-Order Derived Rules Learning） to automatically discover first-order rules for derived predicates from observed plans under an initial domain theory. FODRL is based on the pure inductive learning system FOIL （First-Order Inductive Learning）, which learns a new rule that covers partial positive examples but avoids all negative examples once a time, until all positive examples are covered. However, better than FOIL, FODRL uses activation sets of derived predicates to expand search steps so as to improve the accuracy of learned rules. An activation set is a minimal set of basic facts or predicates which can make a derived predicate hold true under domain rules. The learning process is divided into two steps： first, extract training examples from observed plans then, learn first-order rules for derived predicates which can best fit training examples and the initial domain theory. We experiment in two derived Dlanning domains.PSR and PROMELA. The results show that, with the guidance of an initial domain theory, the rules learned by FODRL are more accurate than those from FOIL, even FOCL （a descendant of FOIL）.