On Geospatial Agents

Firstly, I can see why ALGAs are dominating the GIScience literature on agents. Modeling complex social relationships and migration patterns as well as predator-prey interactions (and more) has a much more compelling and interesting implications for geography (at least on the surface) than does information mining. Even with my limited knowledge of AI agents, I find my mind is flooded with scenarios in which I could apply ALGAs; I grasp the concept of using a computer to model intelligent systems easily.  With that said, I certainly do not wish to underwrite the potential of SGAs. The implications of the ability to work across multiple platforms is somewhat lost on me, and I will attempt to explore in my upcoming lecture with the authors of this piece.

I find most of my difficulty in understanding SGAs and their potential applications lies in what is said by Sieber and Sengupta on page 492. The authors describe how SGAs are divided by tasks while ALGAs are divided by themes. What I gather from this is the following statement: ALGAs are defined by an application, while SGAs are defined within an application.

It seems that these agents certainly have a place in Geography. I have faced more than in one situation in which I felt like a robot data mining unsuccessfully and re-iterating nearly similar interpolations. Another potential use for SGAs crossed my mind, in identifying patterns between z-spectrum graphs in Remote Sensing. My experience with the graphs was that they are very data intensive and uninterpretable.


7:45 pm, 9/28/12


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