CAJ | 학술논문

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 Rules Learning)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.
파생위사시묘술동작비직접효과적주요방식.단시유인류전가설계적파생위사규칙(즉영역이론)불능보증총시정학혹자완비적,인차유시흔난해석일개관찰도적규화해위십요시유효적.결합귀납학습여분석학습적우점,문중제출일충칭위FODRL(First-Order Derived Rules Learning)적산법,재불완미적초시영역이론적인도하종관찰도적규화해중학습일계파생위사규칙.FODRL기우귀납학습산법FOIL(First-Order Inductive Learning),최주요적개진시가이사용파생위사적격활집래확대수색보,종이제고학습도적규칙적정학도.학습과정분위량개보취:선종규화해중제취훈련례,연후학습능구최호의합훈련례화초시영역이론적일계규칙집.재PSR화PROME-LA량개파생규화영역진행실험,결과표명,재대부분정황하FODRL비FOIL(심지포괄기변형산법FOCL)학습도적규칙적정학도도요고.
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 de-rived predicates to expand search steps so as to improve the accuracy of learned rules.An activa-tion 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 exam-ples 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 planning 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).