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一种基于ILP和ASP的学习B语言描述的动作模型方法
引用本文:刘振,张志政.一种基于ILP和ASP的学习B语言描述的动作模型方法[J].计算机科学,2015,42(1):220-226.
作者姓名:刘振  张志政
作者单位:东南大学计算机科学与工程学院 南京211189
基金项目:本文受国家自然科学基金项目(60803061),江苏省自然科学基金项目(BK2008293),东南大学科技基金项目(XJ2008315)资助
摘    要:动作模型学习可以使Agent主动适应动态环境中的变化,从而提高Agent的自治性,同时也可为动态域建模提供一个初步模型,为后期的模型完善和修改提供了基础.通过结合归纳逻辑程序设计(Inductive Logic Program-ming,ILP)和回答集程序设计(Answer Set Programming,ASP),设计了一个学习B语言描述的动作模型算法,该算法可以在混合规模的动态域中进行学习,并采用经典规划实例验证了该学习算法的有效性.

关 键 词:动作模型学习  动作语言B  归纳逻辑程序设计  回答集逻辑程序设计

Learning Action Models Described in Action Language B by Combining ILP and ASP
LIU Zhen and ZHANG Zhi-zheng.Learning Action Models Described in Action Language B by Combining ILP and ASP[J].Computer Science,2015,42(1):220-226.
Authors:LIU Zhen and ZHANG Zhi-zheng
Institution:School of Computer Science and Engineering,Southeast University,Nanjing 211189,China and School of Computer Science and Engineering,Southeast University,Nanjing 211189,China
Abstract:Action model learning is beneficial to autonomous and automated systems.If an Agent can update its action model according to the changes occurred in the environment,it can be more adaptable to the world and operate more effectively.Simultaneously,action model learning can provide modeling dynamic domain with an initial rough model which is the foundation for further improvement and modification.We designed an algorithm used for learning action models described in language B by combining ILP and ASP.This algorithm can work on dynamic domains consisting of objects of different scale.In the experiments,we tested the learning algorithm through using classic planning cases and verified the soundness of the learning algorithm.
Keywords:Action model learning  Action language B  Inductive logic programming  Answer set programming
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