Problem Characteristics in Artificial Intelligence

What is Artificial Intelligence? Problem Characteristics in Artificial Intelligence

Definition:

Artificial Intelligence is a “way of making a computer, a computer-controlled robot, or software think intelligently, in the similar manner the intelligent humans think”.

Since artificial intelligence (AI) is mainly related to the search process, it is important to have some methodology to choose the best possible solution.

To choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.

  1. Is the problem decomposable into small sub-problems which are easy to solve?
  2. Can solution steps be ignored or undone?
  3. Is the universe of the problem is predictable?
  4. Is a good solution to the problem is absolute or relative?
  5. Is the solution to the problem a state or a path?
  6. What is the role of knowledge in solving a problem using artificial intelligence?
  7. Does the task of solving a problem require human interaction?

1. Is the problem decomposable into small sub-problems which are easy to solve?

Can the problem be broken down into smaller problems to be solved independently?

The decomposable problem can be solved easily.

Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.

Is the problem decomposable

2. Can solution steps be ignored or undone?

In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.

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Such problems are called Ignorable problems.

In the 8-Puzzle, Moves can be undone and backtracked.

Such problems are called Recoverable problems.

In Playing Chess, moves can be retracted.

Such problems are called Irrecoverable problems.

Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.

3. Is the universe of the problem is predictable?

In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.

Uncertain outcome!

For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.

For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.

4. Is a good solution to the problem is absolute or relative?

The Travelling Salesman Problem, we have to try all paths to find the shortest one.

Any path problem can be solved using heuristics that suggest good paths to explore.

For best-path problems, a much more exhaustive search will be performed.

5. Is the solution to the problem a state or a path

The Water Jug Problem, the path that leads to the goal must be reported.

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A path-solution problem can be reformulated as a state-solution problem by describing a state as a partial path to a solution. The question is whether that is natural or not.

6. What is the role of knowledge in solving a problem using artificial intelligence?

Playing Chess

Consider again the problem of playing chess. Suppose you had unlimited computing power available. How much knowledge would be required by a perfect program? The answer to this question is very little—just the rules for determining legal moves and some simple control mechanism that implements an appropriate search procedure.
Additional knowledge about such things as good strategy and tactics could of course help considerably to constrain the search and speed up the execution of the program. Knowledge is important only to constrain the search for a solution.

Reading Newspaper

Now consider the problem of scanning daily newspapers to decide which are supporting the Democrats and which are supporting the Republicans in some upcoming election. Again assuming unlimited computing power, how much knowledge would be required by a computer trying to solve this problem? This time the answer is a great deal.

It would have to know such things as:

  • The names of the candidates in each party.
  • The fact that if the major thing you want to see done is have taxes lowered, you are probably supporting the Republicans.
  • The fact that if the major thing you want to see done is improved education for minority students, you are probably supporting the Democrats.
  • The fact that if you are opposed to big government, you are probably supporting the Republicans.
  • And so on …
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Knowledge is required even to be able to recognize a solution.

7. Does the task of solving a problem require human interaction?

Sometimes it is useful to program computers to solve problems in ways that the majority of people would not be able to understand.

This is fine if the level of the interaction between the computer and its human users is problem-in solution-out.

But increasingly we are building programs that require intermediate interaction with people, both to provide additional input to the program and to provide additional reassurance to the user.

The solitary problem, in which there is no intermediate communication and no demand for an explanation of the reasoning process.

The conversational problem, in which intermediate communication is to provide either additional assistance to the computer or additional information to the user.

Summary

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