Posts Tagged ‘Article’
What is Learning by parameter adjustment?
Here the learning system relies on evaluation procedure that combines information from several sources into a single summary static. For example, the factors such as demand and production capacity may be combined into a single score to indicate the chance for increase of production. But it is difficult to know a priori how much weight should be attached to each factor.
The correct weight can be found by taking some estimate of the correct settings and then allow the program modify its settings based on its experience. This type of learning systems is useful when little knowledge is available. In game programs, for example, the factors such as piece advantage and mobility are combined into a single score to decide whether a particular board position is desirable. This single score is nothing but a knowledge which the program gathered by means of calculation.
What is learning by taking advice?
This is a simple form of learning. Suppose a programmer writes a set of instructions to instruct the computer what to do, the programmer is a teacher and the computer is a student. Once learned (i.e. programmed), the system will be in a position to do new things.
The advice may come from many sources: human experts, internet to name a few. This type of learning requires more inference than rote learning. The knowledge must be transformed into an operational form before stored in the knowledge base. Moreover the reliability of the source of knowledge should be considered.
The system should ensure that the new knowledge is conflicting with the existing knowledge. FOO (First Operational Operationaliser), for example, is a learning system which is used to learn the game of Hearts. It converts the advice which is in the form of principles, problems, and methods into effective executable (LISP) procedures (or knowledge). Now this knowledge is ready to use.
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.
Rote learning technique can also be used in complex learning systems provided sophisticated techniques are employed to use the stored values faster and there is a generalization to keep the number of stored information down to a manageable level. Checkers-playing program, for example, uses this technique to learn the board positions it evaluates in its look-ahead search.