Cutting Cycles of Conditional Preference Networks with Feedback Set Approach. (28th June 2018)

• Record Type: Journal Article
• Authors: Liu, Zhaowei; Li, Ke; He, Xinxin
• Other Names: Conforto Silvia Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2018(2018)
• Publication Date: 2018-06-28
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2018/2082875
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : As a tool of qualitative representation, conditional preference network (CP-net) has recently become a hot research topic in the field of artificial intelligence. The semantics of CP-nets does not restrict the generation of cycles, but the existence of the cycles would affect the property of CP-nets such as satisfaction and consistency. This paper attempts to use the feedback set problem theory including feedback vertex set (FVS) and feedback arc set (FAS) to cut cycles in CP-nets. Because of great time complexity of the problem in general, this paper defines a class of the parent vertices in a ring CP-nets firstly and then gives corresponding algorithm, respectively, based on FVS and FAS. Finally, the experiment shows that the running time and the expressive ability of the two methods are compared.