Learning from observation

LEARNING FROM OBSERVATIONS:

Introduction:

What is learning?

Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more effectively the next time (Simon, 1983).

Learning is making useful changes in our minds (Minsky, 1985).

Learning is constructing or modifying representations of what is being experienced

(Michalski, 1986).

A computer program learns if it improves its performance at some task through experience

(Mitchell, 1997).

So what is learning?

(1)         acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);

(2)         discover new knowledge and theories (by creating hypotheses that explain some data or phenomena);

(3)         acquire skills (by gradually improving their motor or cognitive skills through repeated practice,

sometimes involving little or no conscious thought).

(4)         Learning results in changes in the agent (or mind) that improve its competence and/or efficiency.

(5)           Learning is essential for unknown environments, (1)  i.e., when designer lacks omniscience

o                 Learning is useful as a system construction method,

o                 Expose the agent to reality rather than trying to write it down

o                 Learning modifies the agent's decision mechanisms to improve performance

1 FORMS OF LEARNING:

Learning agents:

• Four Components

1.          Performance Element: collection of knowledge and procedures to decide on the next action

E.g. walking, turning, drawing, etc.

2.         Learning Element: takes in feedback from the critic and modifies the performance element accordingly.

3.  Critic: provides the learning element with information on how well the agent is doing based on a fixed performance standard. E.g. the audience

Problem Generator: provides the performance element with suggestions on new actions to take. Components of the Performance Element

•                   A direct mapping from conditions on the current state to actions

•                     Information about the way the world evolves

•                     Information about the results of possible actions the agent can take

•                     Utility information indicating the desirability of world states

Learning element

•                     Design of a learning element is affected by

–Which components of the performance element are to be learned

–What feedback is available to learn these components

–What representation is used for the components

Type of feedback:

–Supervised learning: correct answers for each example

–Unsupervised learning: correct answers not given

–Reinforcement learning: occasional rewards