期刊文献+

扩展的G-KRA抽象模型 被引量:1

Extended G-KRA Abstraction Model
下载PDF
导出
摘要 将广义知识重构与抽象模型框架下的初步感知过程扩展为多重感知过程,根据物理世界W中对象所属域的不同生成多重域抽象模型,并讨论了两种情况下域抽象模型Wi和Wj间的域关系:W的多重域模型Wi和Wj中的构成对象交集为空(即Wi和Wj中不存在有跨域行为的实体);W的多重域模型Wi和Wj中的对象实体存在跨域行为.扩展后的框架R丰富了物理世界W的模型表示,使对象感知由平面感知变为立体感知,应用多种领域知识生成相互关联的不同域模型,增强了模型的推理能力. The primary perception process of the generalized knowledge reformulation and abstraction model was extended to multi-perception process and multiabstraction models were constructed by identifying the domains that the objects of the physical world W belong to. Furthermore, the domain relations between muhiabstraction models Wi and Wj are formally defined from two points of view, i. e. , the intersection of their objects sets is empty (there is no such an entity involved in two different domains) ; there are at least one entity involved in two different domains. The extended G-KRA abstraction model enriches the representation of W compared with the model from a single point of view, it percepts W from the multiple one. Knowledge of various domains can be applied to achieving multiabstraction models and the reasoning ability is enhanced.
出处 《吉林大学学报(理学版)》 CAS CSCD 北大核心 2010年第6期970-974,共5页 Journal of Jilin University:Science Edition
基金 国家自然科学基金重大项目基金(批准号:60496320 60496321) 国家自然科学基金(批准号:60973089 60773097 60873148) 欧盟合作项目(批准号:155776-EM-1-2009-1-IT-ERAMUNDUS-ECW-L12) 吉林省科技发展计划项目(批准号:20100173) 符号计算与知识工程教育部重点实验室开放项目(批准号:93K-17-2009-K05) 吉林省教育厅"十一五"科学技术研究项目(批准号:2009512)
关键词 抽象模型 广义KRA抽象模型 多重抽象模型 域关系 abstraction model general KRA abstraction model muhiabstraction model domain relation
  • 相关文献

参考文献21

  • 1Sacerdoti E.Planning in a Hierarchy of Abstraction Spaces[J].Artificial Intelligence,1974,5:115-135.
  • 2Plaisted D.Theorem Proving with Abstraction[J].Artificial Intelligence,1981,16(1):47-108.
  • 3Giunchiglia F,Walsh T.A Theory of Abstraction[J].Artificial Intelligence,1992,57(2/3):323-389.
  • 4Ellman T.Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximatively Equivalent Objects[C] //International Conference on Machine Learning.Washington:Morgan Kaufmann,1993:104-111.
  • 5Holte R,Mkadmi T,Zimmer R,et al.Speeding up Problem-Solving by Abstraction:A Graph-Oriented Approach[J].Artificial Intelligence,1996,85(1/2):321-361.
  • 6Lowry M.The Abstraction/Implementation Model of Problem Reformulation[C] //Int Joint Conf on Artificial Intelligence.San Franoisco:Morgan Kaufmann Publishers Inc,1987:1004-1010.
  • 7Subramanian D.A Theory of Justified Reformulations[C] //Change of Representation and Inductive Bias.Boston:Kluwer Academic Publishers,1990:147-167.
  • 8Bredeche N,SHI Zong-zhi,Zucker J D,et al.Perceptual Learning and Abstraction in Machine Learning:An Application to Autonomous Robotics[J].IEEE Transactions on Systems.Man,and Cybernetics,Part C:Applications and Reviews,2006,36(2):172-181.
  • 9Giordana A,Saitta L.Abstraction:A General Framework for Learning[C] //Working Notes of the AAAI Workshop on Automated Generation of Approximations and Abstraction.Boston:MA,1990:245-256.
  • 10Knoblock C.Learning Hierarchies of Abstraction Spaces[C] //Proceedings of the 6th International Workshop on Machine Learning.San Francisco:Morgan Kaufmann Pulishers,1989:241-245.

二级参考文献23

  • 1张长胜,孙吉贵,梁书斌,范玮.面向网络的实时飞行模拟系统模型[J].吉林大学学报(信息科学版),2006,24(3):309-315. 被引量:6
  • 2Saitta L,Torasso P,Torta G.Formalizing the Abstraction Process in Modelbased Diagnosis[M].Lecture Notes in Computer Science.Berlin:Springer,2007.
  • 3Sacerdoti E D.Planning in a Hierarchy of Abstraction Spaces[J].Artificial Intelligence,1974,5(2):115-135.
  • 4Plaisted D A.Theorem Proving with Abstraction[J].Artificial Intelligence,1981,16(1):47-108.
  • 5Giunchiglia F,Walsh T.A Theory of Abstraction[J].Artificial Intelligence,1992,57(2/3):323-389.
  • 6Ellman T.Synthesis of Abstraction Hierarchies for Constraint Satisfaction by Clustering Approximatively Equivalent Objects[C]//Proceedings of the 10th International Conference on Machine Learning,Amherst,MA.Washington:Morgan Kaufmann,1993.
  • 7Holte R C,Mkadmi T,Zimmer R M,et al.Speeding up Problem Solving by Abstraction:a Graph Oriented Approach[J].Artificial Intelligence,1996,85(1/2):321-361.
  • 8Lowry M R.The Abstraction/Implementation Model of Problem Reformulation[C]//Int Joint Conf on Artificial Intelligence.Milano:[s.n.],1987:1004-1010.
  • 9Subramanian D.A Theory of Justified Reformulations[C]//Proceedings of the Sixth International Workshop on Machine Learning.Boston:Kluwer Academic Publishers,1990:434-438.
  • 10Bredeche N,SHI Zhong-zhi,Zucker J D.Perceptual Learning and Abstraction in Machine Earning:an Application to Autonomous Robotics[J].IEEE Transactions on Systems.Man,and Cybernetics,Part C:Applications and Reviews,2006,36(2):172-181.

共引文献8

同被引文献8

  • 1WfMC.Workflow Management Coalition Terminology & Glossary. Document Number WFMC-TC-1011 . 1999
  • 2L.Saitta,J.Zucker."Semantic Abstraction for Concept Representation and Learning,"[].ProcSARA.1998
  • 3Salimifard K,Wright M.Petri net-based modelling of workflow systems: An overview[].European Journal of Operational Research.2001
  • 4Bajaj A,Ram S.SEAM: A state-entity-activity-model for a well-defined workflow development methodology[].IEEE Transactions on Knowledge and Data Engineering.2002
  • 5van der Aalst WMP,van Hee KM.Workflow Management:Models,Methods,and Systems[]..2002
  • 6J.Tick.Workflow Model Representation Concepts[].Proceed ings ofth International Symposium of Hun garian Researchers on Computational IntelligenceHUCI.2006
  • 7Jozsef Tick.Workflow Modeling Based on Process Graph[].Proceedings ofth Slovakian-Hungarian Joint Symposium on Ap plied Machine Intelligence and Informatics.2007
  • 8孙善武,王楠,欧阳丹彤.广义KRA抽象模型[J].吉林大学学报(理学版),2009,47(3):537-542. 被引量:8

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部