As banks, financial services, insurances, and economic research units worldwide strive to add knowledge based capabilities to their analyses and services, or to create new ones, this volume aims to provide them with concrete tools, methods and application possibilities. The tutorial component of the book relies on case study illustrations, and on source code in some of the major artificial intelligence languages. The applications related component includes an extensive survey of real projects, and a number of thorough generic methods and tools for auditing, technical analysis, information screens and natural-language front-ends. The research related component highlights novel methods and software for economic reasoning under uncertainty and for fusion of qualitative/quantitative model-based economic reasoning.

The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form "find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. " Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.