摘要
知识空间理论是当前粒计算领域中的研究热点。对知识空间中问题代表的技能进行分析是构造知识空间及进行知识评价的一种重要方法。通过消除问题当中冗余的技能可以有效简化问题的处理,降低计算机处理的复杂度。在知识空间理论中,当技能映射模型是析取模型时,技能之间对于问题的解决表现出的是或的关系。通过类比粗糙集理论中属性约简的方法,提出了一种析取模型下最小技能集的生成方法。从粒计算的视角,将知识空间理论与粗糙集理论建立起了有意义的联系。
Knowledge space theory（KST） is a current research focus in the granular computing field. Analyzing the skills represented by questions in KST is an important method of constructing knowledge space and knowledge assessment. Eliminating the redundant skills can simplify the process of problems and reduce the computational complexity effectively. There is a disjunctive model of skills map based on which the relation between the skills that solve the same problem is or-relation. This paper proposes a method of generating the minimal skill set by anlehnunging the process of attribute reduction in rough set theory（RST）, and establishes a meaningful connection between KST and RSet theory from the granular computing perspective.
出处
《计算机科学与探索》
CSCD
2010年第12期1109-1114,共6页
Journal of Frontiers of Computer Science and Technology
基金
国家自然科学基金No.60475019
60970061
国家博士学科点专项科研基金No.2006247039~~
关键词
知识空间
粒计算
最小技能集
knowledge space
granular computing
minimal skill set
