Abstract:

The developing of intelligent automated generalization has been a challenging problem to be solved because of the complex thinking of human beings. A new approach of intelligent road-network selection using cases based reasoning (CBR) was put forward in this paper. In this approach, learning and cognition techniques of human being in artificial intelligence were used to establish, learn and reason the cases of cartographers. First, it took a certain area’s road-network selection result achieved from interactive selection of cartographic experts as reference templates, and transformed the templates into selection cases after establishing the description structure of cases. Second, the cases were analyzed and reasoned with enhanced simplifying and generalizing methods so as to get more effective case model base. Finally, the computer finished the similar road selection using CBR technique supported with the enhanced case model base. Compared with the past algorithms, the new approach uses enhanced road selection cases to simulate the thinking of human being, and CBR model to select similar road-work intelligently, which fetches up the shortcoming of intelligence of traditional road selection methods, and creates a new embranchment in the field of intelligent automated generalization. Examples and related analyzing and assessing results illustrate the scientificity and usability of the new approach. And further works to be improved are also suggested at the end of this paper.

Key words: road-network, case, case model base, case based reasoning (CBR), intelligent road-network selection