Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (2): 29-39.DOI: 10.3778/j.issn.1002-8331.1709-0450

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Learning temporal operator model from partially observed environment

LIU Sheng, ZHAO Tongzhou, CAI Dunbo   

  1. Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan 430205, China
  • Online:2018-01-15 Published:2018-01-31


刘  胜,赵彤洲,蔡敦波   

  1. 武汉工程大学 智能机器人湖北省重点实验室,武汉 430205

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

摘要: 已有的动作模型学习方法针对确定的或不确定的瞬时动作,而未考虑动作模型中的时态关系。提出了在部分观测环境下自动学习时态动作模型的方法。设计了学习动作持续时间表达式一般形式的两阶段线性回归方法。通过分析命题时间戳设计了动作前提、效果与动作之间时态关系算子的构建算法。在“国际智能规划竞赛”的规划问题集上进行了实验,结果表明了该方法的有效性。

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