COMPUTER ORIENTED LEARNING PROCESSES

Rote learning.We can keep all the situations already found. With each situation we store an indication on its interest or the move which has to be played. Samuell gives an example of such an application. This can be done if there are not too many possible situations. Even in games where there are many possible situations, this method can be useful for the beginning or the end of the games. We can improve this method: if the rulesare the same for all the players, we can standardize the situations: we assume that it is always the same player who has to play; for instance, at chess, white. We just keep half of the possible situations. At Go Moku where there are two axes of symetry, we just keep a quarter of the situations. But even with these improvements, there are many cases where this method is not useful because there are too many situations. It is doubtful that we can have good results in the middle game 398 at chess with such methods. We can try to generalize what has been done in a situation to another similar situation. For example in the second situation we play the same move than in the first one. Waterman2 has written an interesting progrpi playing poker. Let us describe it roughly. A situation is described by the value of seven variables: value of the program's hand, amount of money in the pot, measure of conservative style by the opponent... The program defines a partition of the set of possible values of these variables. For instance: If our hand is excellent, bet low if the opponent tends to be a conservative player and has just bet low. The problem is to define wisely these subsets. This can be done by the program which improves progressively the quality of the partition. This method is good for poker and it obtained very good results. But it is difficult to see how we can use it in a game like chess. How could we evaluate the similarity between situations, such as in similar situations we have to play the same move? A different position of a pawn can destroy a combination.