"Rote Learning" artificial intelligence learning .

"Rote Learning"

Rote learning is the basic learning activity. It is also called memorization because the knowledge, without any modification is, simply copied into the knowledge base. As computed values are stored, this technique can save a significant amount of time.

problem-solving mechanisms and the knowledge representation techniques that were presented Rote learning .

Rote learning :-

When a computer stores a piece of data, it is performing a rudimentary form of learning. After all, this act of storage presumably allows the program to perform better in the future.

0 data caching, we store computed values so that we do not have to recompute them later. When computation is more expensive than recall, this strategy can save a significant amount of time. Caching has been used in programs to produce some surprising performance improvements.

game-playing programs, Samuel’s checkers Perform. This program learned to play checkers well enough to beat its creator.

 Program used the minimax search procedure to explore checkers game trees.

When it could search no deeper, it applied its static evaluation function to the board position and used that score to continue its search of the game tree. When it finished searching the tree and propagating the values backward, it had a score for the position represented by the root of the tree, It could then choose the best move and make it. But it also recorded the board position at the root of the tree and the backed up score that had just been computed for it. This situation is shown in in pic (A)

Now suppose that in a later game, the situation shown in pic (B) were to arise. Instead of using the static evaluation function to compute a score for position A, the stored value for A can be used. This creates he effect of having searched an additional several ply since the stored value for A was computed by backing up values from exactly such a search. 

         

                                    (A)

                    

                                      (B)

Rote learning of this sort is very simple. It does not appear to involve any sophisticated problem-solving capabilities. But even it shows the need for some capabilities that will become increasingly important in more complex learning systems. These capabilities include:

Organized Storage of Information-

In order for it to be faster to use a stored value than it would be to tecompute it, there must be a way to access the appropriate stored value quickly. In Samuel’s program, this was done by indexing board positions by a few important characteristics, such as the number of pieces. But as the complexity of the stored information increases, more sophisticated techniques are necessary.

Generalization-

The number of distinct objects that might potentially be stored can be very large. To keep the number of stored objects down to a manageable level, some kind of generalization is necessary. In Samuel’s program, for example, the number of distinct objects that could be stored was equal to the number of different board positions that can arise in a game. Only a few simple forms of generalization were used in Samuel’s program to cut down that number. All positions are stored as though White is to move. This cuts the number of stored positions in half. When possible, rotations along the diagonal area also combined. Again, though, as the complexity of the learning process increases, so too does the need for generalization.

At this point, we have begun to see one way in which learning is similar to other kinds of problem solving suCcess depends on a good organizational structure for its knowledge base.