Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"p...Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"plan traces".To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al.展开更多
为了达到组织目标和任务使命,必须建立一系列彼此相互关联的、具有层次结构的活动和过程之间的关系。要实现对组织的有效管理,核心就在于通过计划建模和控制系统来协调这些关系。在介绍与分析SysML语言特点的基础上,建立了作战行动序列(...为了达到组织目标和任务使命,必须建立一系列彼此相互关联的、具有层次结构的活动和过程之间的关系。要实现对组织的有效管理,核心就在于通过计划建模和控制系统来协调这些关系。在介绍与分析SysML语言特点的基础上,建立了作战行动序列(COA,Course of Action)的形式化定义,提出了基于SysML的作战行动序列建模方法,并给出了应用实例。应用该建模方法,有利于提高作战行动计划的适应性和开放性。展开更多
目的:探讨将PDCA管理模式应用于不明原因发热(fever of unknown origin,FUO)诊断中,以提高FUO的诊治水平。方法:通过在FUO临床实践中应用PDCA管理环,提高FUO诊治行为的计划性,提高FUO病因诊断的能力。结果:应用PDCA管理模式,可以规范FU...目的:探讨将PDCA管理模式应用于不明原因发热(fever of unknown origin,FUO)诊断中,以提高FUO的诊治水平。方法:通过在FUO临床实践中应用PDCA管理环,提高FUO诊治行为的计划性,提高FUO病因诊断的能力。结果:应用PDCA管理模式,可以规范FUO的诊治,提高FUO临床诊治水平。结论:PDCA管理模式在FUO中应用,促进了FUO病因诊断的能力提高,促进了治愈率的提高。展开更多
基金Supported by the National Natural Science Foundation of China(61103136,61370156,61503074)Open Research Foundation of Science and Technology on Aerospace Flight Dynamics Laboratory(2014afdl002)
文摘Action model learning has become a hot topic in knowledge engineering for automated planning.A key problem for learning action models is to analyze state changes before and after action executions from observed"plan traces".To support such an analysis,a new approach is proposed to partition propositions of plan traces into states.First,vector representations of propositions and actions are obtained by training a neural network called Skip-Gram borrowed from the area of natural language processing(NLP).Then,a type of semantic distance among propositions and actions is defined based on their similarity measures in the vector space.Finally,k-means and k-nearest neighbor(kNN)algorithms are exploited to map propositions to states.This approach is called state partition by word vector(SPWV),which is implemented on top of a recent action model learning framework by Rao et al.Experimental results on the benchmark domains show that SPWV leads to a lower error rate of the learnt action model,compared to the probability based approach for state partition that was developed by Rao et al.
文摘为了达到组织目标和任务使命,必须建立一系列彼此相互关联的、具有层次结构的活动和过程之间的关系。要实现对组织的有效管理,核心就在于通过计划建模和控制系统来协调这些关系。在介绍与分析SysML语言特点的基础上,建立了作战行动序列(COA,Course of Action)的形式化定义,提出了基于SysML的作战行动序列建模方法,并给出了应用实例。应用该建模方法,有利于提高作战行动计划的适应性和开放性。
文摘目的:探讨将PDCA管理模式应用于不明原因发热(fever of unknown origin,FUO)诊断中,以提高FUO的诊治水平。方法:通过在FUO临床实践中应用PDCA管理环,提高FUO诊治行为的计划性,提高FUO病因诊断的能力。结果:应用PDCA管理模式,可以规范FUO的诊治,提高FUO临床诊治水平。结论:PDCA管理模式在FUO中应用,促进了FUO病因诊断的能力提高,促进了治愈率的提高。
