A knowledge-based creation of mathematical programming for GIS problem solving
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05.
Nowadays the biggest challenge to GIS is the model design of theme analyzing. Many GIS applications need more functions in analysis and decision-making than in data organization or visualization. While confronted with an application like planning a new metro line in a city, the typical GIS cannot accomplish it by itself, unless some human experts or artificial intelligence technology are involved. Numbers of GIS applications essentially are models of theme analyzing, and they are also examples
... are also examples of problem solving in AI. Therefore many theories and technologies from AI can be embedded in GIS to strengthen its ability of automatic analysis. Fortunately mathematical programming is a kind of powerful tool to settle problems with a demand of optimization under some constraint conditions. Many GIS applications happen to be this type, and some mathematical models can usually approximate them accurately. Two new questions aroused. The first is what objective function and constraint conditions that constitute a mathematical programming should be, and the second is how to acquire those mathematical expressions. Due to the establishment of a mathematical programming is highly knowledge-based, so an AI subsystem is designed to autonomously create a mathematical programming. This problem has a typical characteristic of multiple domains crossing from perspective of Knowledge Engineering, so some novel knowledge-processing methods, like knowledge dependence, knowledge transition and knowledge reference, are studied. And a knowledge system represented by frames and a reference-based knowledge-using method are presented here.