How can we learn?

Many approaches have been taken to attempt to provide a machine with learning capabilities. This is because learning tasks cover a wide range of phenomena.

Listed below are a few examples of how one may learn. We will look at these in detail shortly

Skill refinement

-- one can learn by practicing, e.g playing the piano.

Knowledge acquisition

-- one can learn by experience and by storing the experience in a knowledge base. One basic example of this type is rote learning.

Taking advice

-- Similar to rote learning although the knowledge that is input may need to be transformed (or operationalised) in order to be used effectively.

Problem Solving

-- if we solve a problem one may learn from this experience. The next time we see a similar problem we can solve it more efficiently. This does not usually involve gathering new knowledge but may involve reorganisation of data or remembering how to achieve to solution.

Induction

-- One can learn from examples. Humans often classify things in the world without knowing explicit rules. Usually involves a teacher or trainer to aid the classification.

Discovery

-- Here one learns knowledge without the aid of a teacher.

Analogy

-- If a system can recognise similarities in information already stored then it may be able to transfer some knowledge to improve to solution of the task in hand.