Learning
Learning
is the sub field of AI and intersects with cognitive science, information
theory, and probability theory, among others. Machine learning is the study of
how to build computer system that adapt and improve with experience. It
represents inductive reasoning. Learning is one of the most important
characteristics an agent must posses in order to claim intelligent.
Definition
by Simon (1983)
“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 efficiently and more effectively the next time.”
A general model of learning agent
A
learning agent can be divided into four conceptual components as shown in fig
below:
1) Critic:
§ The critic tells the
learning element how the agent is doing with respect to a fixed performance
standard
§ Critic is necessary
because the percepts themselves provide no indication of the agent success e.g.
a chess program could receive a precept indicating that it has checkmated its
opponents but it needs a performance standard to know that this is good thing;
the percept itself does not say so.
2)
Learning Element:
§ Responsible for making
improvements
§ Design of learning
element depends very much on the design of the performance element.
3) Performance
Element:
§ Performance element is
responsible for selecting external actions (It takes in percepts and decide on
actions).
4)
Problem Generator:
§ Responsible for
suggesting actions that will lead to new and informative experiences.
§ It helps the agent to
explore a little so that it might discover better actions for certain
situations. The problem generator suggests such a explanatory actions. This is
what scientists do when they carry out experiments. Galileo did not think that
dropping rocks from the top of tower of Pisa was valuable itself. He was
not trying to break the rocks, nor to modify the brains of unfortunate
passer-by. His aim was to modify his own brain, by identifying a better theory
of the motion of objects.
Types of Learning
1) Rote
learning:
§ method of study based on
learning facts etc by heart without considering their meaning.
§ most trivial form of
learning
§ simple storing of
computed information (Everything is memorized ) e.g. computer simply stores a
piece of data in knowledge base.
§ Many computer program
e.g. database systems can be said to learn. The act of storage allows the
program to perform better in future.
§ There may be situations
where computation is more expensive than recalling the previously done job.
Thus remembering the previous would certainly help performance better. This
form of learning is called rote learning.
Capabilities
of rote learning
Though
rote learning is very simple and does not require sophisticated problem solving
capabilities, it should have following capabilities
§ Organize storage of
information: there must be a mechanism to access the appropriate information
very quickly
§ Generalization: to keep
the number of stored information down to a manageable number, the number of
distinct objects must be generalized and stored.
2.
Learning by taking advice
§ A computer can do very
little without a program for it to run. When a programmer writes a series of
instructions into a computer, a rudimentary kind of learning is taking place.
§ In this scenario, the programmer
is a kind of teacher and the computer is a learner, Now if the program is
written in high level language, it needs to be interpreted and compiled before
it can be executed. Thus it is necessary for the system to operationalize the
knowledge before it can use it.
§ This form of learning
where the teacher gives instruction into high level language and the system
converts it to machine understandable form and uses in problem solving is known
as “learning by taking advice”.
3)
Inductive Bias Learning (Learning by Example)
§ The real world problem
domain tends to be very large. Hence, a learner usually only examines a
fraction of all possible examples. From this limited experience, the teacher
must generalize correctly to unseen instance of the domain. This is the problem
of induction.
Inductive
Bias
§ Refers ti any criteria a
learner uses to constrain the concept space or to select concepts within that
space
§ Example- For a certain
football , we have properties like round, big, and heavy an for certain ball like
ping pong bal, we have properties like round, small, light bouncy.
§ Now from the above
information, the knowledge base should extract certain properties which
uniquely identify the ping pong ball. The learner can use a bias that does not
consider the color of the object and that consider only the shape and bouncing
properties of the object. The properties that must be stored are : ” Round and
Bounce”
4)
Winston’s learning Program
§ structural concept of
learning program
§ This program operated in
a simple block world domain. Its goal was to construct representations of the
definitions of concepts in the block domain.
Learning Algorithm
1)
Supervised Learning
§ a teacher or oracle is
available which provides a desired action corresponding to a perception.
§ provides training set
2)
Unsupervised Learning
§ no teacher is available
§ learner only discovers
persistent patterns in the data consisting of a collection of perception
§ also called exploratory
learning
3)
Active Learning
§ Here not only a teacher
is available, the learner has the freedom to ask the teacher for suitable
perception-action example pairs which will help the learner to improve its
performance.
4)
Reinforcement Learning
§ a teacher is available,
but the teacher instead of directly providing the desired action corresponding
to a perception, return reward and punishment to the learner for its action
corresponding to a perception.
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