Artificial intelligence in geography
This unique work introduces the basic principles of artificial intelligence with applications in geographical teaching and research, GIS, and planning. Written in an accessible, non-technical and witty style, this book marks the beginning of the Al revolution in geography with major implications for teaching and research. The authors provide an easy to understand basic introduction to Al relevant to geography. There are no special mathematical and statistical skills needed, indeed these might well be a hindrance. Al is a different way of looking at the world and it requires a willingness to experiment, and readers who are unhindered by the baggage of obsolete technologies and outmoded philosophies of science will probably do best. The text provides an introduction to expert systems, neural nets, genetic algorithms, smart systems and artificial life and shows how they are likely to transform geographical enquiry.
* A major methodological milestone in geography
* The first geographical book on artificial intelligence (Al)
* No need for previous mathematical or statistical skills/knowledge
* Accessible style makes a difficult subject available to a wide audience
* Stan Openshaw is one of the world s leading researchers into geographical computing, spatial analysis and GIS.
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Artificial intelligence and geography
A brief history of artificial intelligence
Heuristic search in geography
8 other sections not shown
applications approach areas Artificial Intelligence artificial neural networks basic become behaviour better biological bit string brain building cellular automata classification clusters complex computer systems context create database defined destination developed distance distance decay Environment and Planning example exist expert systems extremely fuzzy logic fuzzy model fuzzy sets fuzzy systems generalisation genetic algorithms geographical analysis handle hardware heuristic search highly IKBS important input interest knowledge base large number learning machine intelligence machine learning membership functions Monte Carlo neural nets neural network neurocomputing neurons nonlinear Openshaw optimal output paradigm parallel parallel computer parameters patterns performance possible potential probably random relevant representation revolution rules search methods search problem search process simple simulated simulated annealing smart solution solve spatial analysis spatial data spatial interaction models statistical Step Table tabu search task values variables whilst zone