Problem Solving Agent

Problem Solving Agent-57
Instead, a more abstract notion of checkmate is used, whereby our agent checks that the opponent's king cannot move without being captured.Abstraction is an important tool for scientists in general, and AI practitioners in particular.

Instead, a more abstract notion of checkmate is used, whereby our agent checks that the opponent's king cannot move without being captured.Abstraction is an important tool for scientists in general, and AI practitioners in particular.

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For instance, there are some tasks which are single problems solved by searching, e.g., find a route on a map.

Alternatively, there are tasks such as winning at chess, which have to be broken down into sub-problems (searching for the best move at each stage).

For example, in chess, an operator takes one arrangement of pieces on the board to another arrangement by the action of the agent moving a piece.

It is essential when designing a problem solving agent to know when the problem has been solved, i.e., to have a well defined goal test.

Suppose the problem we had set our agent was to find a name for a newborn baby, with some properties.

In this case, there are lists of "accepted" names for babies, and any solution must appear in that list, so goal-checking amounts to simply testing whether the name appears in the list.

As your search agent becomes more sophisticated, you may give it more information to produce better routes.

For example, the population of a town may affect the volume of traffic, which will affect the time taken to drive through the town.

If you are hired as an AI researcher/programmer, you will be expected to come armed with a battery of AI techniques, many of which we cover later in the course.

However, perhaps the most important skill you will bring to the job is to effectively seek out the best way of turning some vague specifications into concrete problems requiring AI techniques.

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