Problem Solving Agent

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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.

Rather, the point of the search is to find a path, so the agent must remember where it has been.

The answer is, of course: (FILL IN THIS GAP AS AN EXERCISE).

One form of abstraction that is very useful is to disregard particulars of a problem.

For instance, when designing an agent to find a route from one town to another, you ignore most of the details you may know about each town (population, weather, food, etc).The series of actions that the agent actually performs is its search path, and the final state is a solution if it has the required property.There may be many solutions to a particular problem.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.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.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.Before we worry about exactly what strategy to use, the following need to be taken into consideration: Broadly speaking, there are two different reasons to undertake a search: to find an artefact (a particular state), or to find a path from one given state to another given state.Whether you are searching for a path or an artefact will affect many aspects of your agent's search, including its goal test, what it records along the way and the search strategies available to you.


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