Geography Reference
In-Depth Information
In some respect understanding and answering the request is not a too difficult
question for an intelligent system either. An intelligent system is superior to humans
in computing routes. It computes faster, processes more data, and produces more
accurate results. An intelligent system can for example guarantee to compute the
quickest route, and perhaps even include data about current traffic conditions,
something our person in the example above cannot do. But to do so the system
must know start and destination. If the intelligent system runs on the user's smart
phone determining the start is possible using the sensors on board. But to determine
the destination the system has to resolve this word 'airport', which is surprisingly
tricky.
Obviously, 'airport' is ambiguous. The system knows many airports. Which one
is the one the user has in mind? Choosing the most prominent one (as search engines
are good at) would guide everybody to Atlanta, currently the world's largest airport.
But this is probably not what the user had in mind. Choosing the one nearest to the
user would be a better guess, but in the concrete example this would lead to a local
airfield, not the international airport. Choosing the one most frequently visited by
this person (another option for machine learning) would also be inappropriate. No,
in this particular case it is the one where the inquirer wants to depart with a flight in a
couple of hours from now. An intelligent system could determine that the function
of an airport is to provide air travel, that air travellers need valid tickets bought in
advance, and thus, could check whether this user has such a ticket to identify the
airport. This is not only quite a complex reasoning chain, it requires also to assess
all the various suggestions and reject the less likely ones.
After a route has been selected by the system, it has to communicate it in a way
easy to understand and memorize by the user. Assume that the system is aware of
the benefits of communicating by landmarks, then it has a challenge in selecting
landmarks for this purpose. While a person needs not think twice when picking the
'hospital', an intelligent system knows thousands of objects along the route, of a
variety of types and spatial granularities, from suburbs to ATMs, garbage bins and
light poles. What is a good landmark? The one in the most outstanding color? A fire
hydrant; not a good choice for a car driver, and too many of them along the route.
The largest one? The best known one? Probably one of the objects at decision points,
but which one? And is the landmark unambiguous (“the hospital”) or ambiguous
(“a hospital”)? Is the landmark known to the inquirer, or at least recognizable in
its identity, such that the system can use its name (“Royal Melbourne Hospital”)?
If so, does the inquirer also know how to find this landmark such that the first
part of the route can be folded into a simple instruction: “You know how to find
back to this later. Or can the inquirer at least recognize the type, as a hospital can
typically be identified in its function from the outside by signs and certain functions
at ground level? If not, should the landmark be described by its appearance: “A tall
building on your right that is actually a hospital”? That is to say an intelligent system
should integrate knowledge about the context of the enquiry, knowledge about the
appearance of objects, analytical skills with respect to the route, and knowledge
about the familiarity of the inquirer with the environment.
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