Issue No. 01 - January (2006 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.2
This paper presents a new complicated-knowledge representation method for the self-reconfiguration of complex systems such as complex software systems, complex manufacturing systems, and knowledgeable manufacturing systems. Herein, new concepts of a knowledge mesh (KM) and an agent mesh (AM) are proposed along with a new KM-based approach to complicated-knowledge representation. KM is the representation of such complicated macroknowledge as an advanced manufacturing mode, focusing on knowledge about the structure, functions, and information flows of an advanced manufacturing system. The multiple set, KM, and the mapping relationships between both, are then formally defined. The union, intersection, and minus operations on the multiple sets are proposed, and their properties proved. Then, the perfectness of a KM, the redundancy set between the two KMs, and the multiple redundancy set on the redundancy set are defined. Three examples are provided to illustrate the concepts of the KM, multiple set, multiple redundancy set, and logical operations. On the basis of the above, the KM-based inference engine is presented. In logical operations on KMs, each KM is taken as an operand. A new KM obtained by operations on KM multiple sets can be mapped into an AM for automatic reconfiguration of complex software systems. Finally, the combination of two real management modes is exemplified for the effective application of the new KM-based method to the self-reconfiguration of complex systems. It is worth mentioning that KM multiple sets can also be taken as a new formal representation of software systems if their corresponding AMs are the real software systems.
Index Terms- Knowledge and data engineering tools and techniques, complicated knowledge representation, knowledge mesh, agent mesh, formal representation of software systems.
Hong-Sen Yan, "A New Complicated-Knowledge Representation Approach Based on Knowledge Meshes", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 47-62, January 2006, doi:10.1109/TKDE.2006.2