Summary
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The main goal of the proposed project is to develop a theoretical
framework for adaptive map-based wayfinding systems. A map represents reality by abstracting from many
aspects of the represented world. But what is the optimal level of
abstraction? Maps are used for handling a number of different tasks:
self localization, wayfinding, planning movements, geographic reasoning
and others. Is it possible to solve all these tasks optimally by means of
the same map? A basic assumption of the project is that schematization can
facilitate human diagrammatic reasoning, but there are also levels of schematization that
make map-like representations unusable. In this project we will
develop a formal description of levels of schematization, i.e. we will
identify schematization processes that yield suitable spatial abstractions
for handling a given task. This will allow maps to focus on task relevant
aspects and explain the benefits of map simplification (e.g. through spatial
schematization) in comparison to spatial veridicality. We propose to combine methods and approaches from computer
science, geography, and cognitive psychology to develop a representational
theory of schematic maps. Methods from computer science will provide a formal
specification of the correspondences between schematic representations and the
spatial environment. The computational methods will be complemented by
geographic and psychological studies on appropriate levels of abstraction, on
how humans interpret schematic maps, and which maps
provide the most effective use in solving a given task. This theory is the basis
for a processing model that allows for efficient wayfinding support based on
utilizing schematic maps.
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