中文摘要：

在动作间的状态未知条件下,利用遗传算法,从不完整的领域描述和规划实例中学习动作模型,并且设计了AMLS-GA（Action Model Learning System Based on Genetic Algorithm）系统来具体实现这一思想.作者为每一个动作构建一个可能谓词集,这个谓词集覆盖了动作前提表、增加表和删除表中的所有谓词.采用二进制编码的方式,把动作模型编码成GA搜索空间中的一个假设,学习过程是在标准的遗传算法框架下进行的.把学习结果的正确性定义为尽可能多的解释规划实例,并且通过实验的方法对比学习到的模型与专家预定义模型之间的差别.实验结果表明,算法能在较短的时间内,学习到一个逼近专家描述的动作模型.

英文摘要：

Intelligent Planning and Machine Learning are two hot topics in AI research field. Integrated research in these two topics has gained increasing focus. Based on the assumption that no intermediate states between actions are given, this paper presents algorithms to learn action model from incomplete domain description and existent plan examples using genetic algorithm （GA）. We further develop a system called AMLS-GA （Action Model Learning System Based on Genetic Algorithm） to evaluate this method. It builds a possible predicate set for each partial described action, which covers all possible predicates in precondition, add list and delete list. It encodes the action model as a hypothesis in GA search space exploiting binary coding. The whole learning process is under the standard GA framework proposed by Professor Holland in Michigan University. We define the correctness of this method as explaining more plan examples, and make comparison between the learned model and the model described by experts. Experimental results show that the algorithm can learn an action model close to expert formalism in reasonable time.