关键词: 智能规划, 动作模型学习, 时态关系, 线性回归, 领域建模, 知识获取

Abstract: There exist several successful action model learning methods for instaneous actions with certain or uncertain effects. However, few of them consider the temporal relationships underlying domain models for more realistic environments. The paper proposes a method for the automatically learning of temporal action model in the settings of partially observable environment. The general form of a duration’s expression is learnt by a two-stage linear regression method. The temporal relationships among preconditions, effects and operators are constructed by analysis on propositions’ timestamps in plan traces. Experiments on the temporal planning benchmarks from the International Planning Competition （IPC） are carried out and the results show that the method is effective.

Key words: automated planning, action model learning, temporal relation, linear regression, domain modeling, knowledge acquisition