Geospatial Agents, Agents Everywhere by Sengupta and Sieber (2007) qualifies the distinction of geospatial agents in Artificial Intelligence (AI) research as well as distinguishes between Artificial Life Geospatial Agents (ALGAs) and Software Geospatial Agents (SGAs). Since I do not have much experience with ALGAs, I began thinking about SGAs and as I was reading this I kept going back to various instances during my time at McGill where I had any exposure to SGAs, and one time stands out in particular. During GEOG 307 we had a reading on location-allocation based modeling and shortest path analysis called Flaming to the scene: Routing and locating to get there faster by Figueroa and Kartusch (2000) where the Regina fire department did a Fire Station Location Study as well as built a program to identify the best routes to achieve the fastest response times. Sengupta and Sieber (2007) are concerned with highlighting the legitimacy of these two AI traditions and the importance of geospatial agents’ ability to work with geospatial data specifically as well as it relevance to GIScience. They mention its applicability to social science problems and I immediately thought of the Fire Station Location Study as an example of a SGAs used to solve a real world concern. However, my certainty of this as an SGA is not as strong once I considered the problem of autonomy. The researchers were capable of letting the simulation run to determine an output but they had predetermined all of the necessary inputs from municipal data beforehand. The authors do address the problem of autonomy for ALGAs and SGAs in AI research but they really only distinguish between a strong and weak level of autonomy. It seems to me that defining a level of autonomy is extremely subjective, and though a necessary qualifier for a program to be considered in the realm of AI, it may not be the best measure. Perhaps the field of AI research would benefit from further elaboration on what is truly autonomous?
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