What is Qualitative Spatial Reasoning
I’m working on a new project in the field of bio-informatics, about which I will write at a later point. As part of that project, I’m looking at spatio-temporal reasoning. I did some high level research and eventually found the field of qualitative spatial reasoning (QSR), a subfield of AI. QSR seems to provide solutions to problems similar to ours and I decided to explore further.
I will share what I learn and hopefully you will find it useful in your own research. The goal of Qualitative Reasoning (QR) is to enable computers/programs to reason about physical systems without the need for precise quantitative information [1]. Numerical simulations, for example, use precise data. QR is concerned with representing, problem solving and reasoning about the (physical) world. QR is something all of us do at some point during the day: merging into traffic (we do not know the exact speed other cars are traveling at or exactly how far away they are, yet we are able to merge without accidents). In determining whether it is save to merge, we use qualitative knowledge instead of precise quantitative knowledge. Despite of having imprecise data and sometimes even missing data, we are able to navigate the roads successfully.
QSR is an subfield o QR that is concerned with qualitative reasoning in space. For example, imagine you are at the zoo, talking to a friend about birds in the cage. Clearly, you are unable to measure exact distanced, but you are able to describe what you are looking at by saying things like “The blue bird on the top, left back of the cage.” This would be a QSR representation of the location of the blue bird.
QSR is used by geographical information systems (GIS), robotic vision, bio-informatics and many others. Next, I will talk about some existing languages that are used in QSR, particularly, RCC-8. What other areas of reasearch make use of QR and/or QSR? Can you imagine some other applications?