Introduction to Learning:
Learning in
Artificial Intelligence plays an important role and the implementation of Human
learning concepts in Machine has brought a tremendous change in the field of
Artificial Intelligence. The Learning capabilities of human beings and
implementing the same in Machines to get better performance is not comparable.
Sooner the IT industry will bring revolution as already you have seen various
models working based on various Machine learning Algorithms and these models
are cost-effective, convenient to use, and save time.
Definition of
Learning by various Father of Artificial Intelligence
▰ Herbert A. Simon- 1983
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
▰ Marvin
Minsky,1985
Learning
is making useful changes in our minds (Minsky, 1985).
▰ Ryszard
S. Michalski1986:
Learning
is constructing or modifying representations of what is being experienced.
Learning
modifies the agent's decision mechanisms to improve performance.
▰ Tom Mitchell, 1997
The computer program learns if it improves its performance at some tasks through
experience. He proposed:
"
A computer program is said to learn from experience 'E' with respect to some
class of tasks 'T' and performance measure 'P', if its performance at tasks in
'T', as measured by 'P', improves with experience E ".
▰ Arthur
Samuel,1959
Machine learning
is the subfield of computer science, that gives computers the ability to learn
without being explicitly programmed".
Definition of
Learning:
Learning is
defined as a specialized form of Knowledge acquisition. Learning is
constructing or modifying the representation of what is being experienced. Intelligent
Agents works on the basis of information provided by programmer whereas
Learning Agents have some learning capabilities, it tries to learn from the
past experiences and observations. Learning modifies the agent’s decision
mechanism to improve performance. We know that adding Human Intelligence to
machines is Artificial intelligence. To solve any problem, the Machine uses some
facts and concludes on the basis of rules generated from facts. The same is been
fed to the machine and it works on the basis of knowledge acquisition and
learnings.
▰ Introduction
to Machine Learning
The primary aim
of Learning in Artificial Intelligence is to allow the computers or Machines to learn
automatically without human intervention or assistance and adjust actions
accordingly. This is the reason this concept is known as Machine Learning.
Machine learning
focuses on the development of computer programs that can access data and use it
learn for themselves.
The main purpose
of machine learning is to study and design the algorithms that that provides
systems the ability to automatically learn and improve from experience without
being explicitly programmed.
Machine Learning
is an application of Artificial Intelligence that can be used to produce the
predicates from the given dataset. The process of learning begins with
observations or data, such as examples, direct experience, or instruction, in
order to look for patterns in data and make better decisions in the future
based on the examples that we provide.
Learning
Components:
There are six
Components out of which four are the Core Components
1.Performance
Element
2.Learning
Element
3. Critic
4. Problem
Generator
5.Sensors
6.Actuators/Effectors
1. Performance Element or
Component:
▰ The Performance Element is the agent that acts in the world. This element percepts
the input from the environment and decides what to do on perceived input and
performs external actions.
▰ It
has a collection of knowledge and procedures stored in the form of Knowledge Base
to decide on what should be the next action
E.g., In the case of
Robots: walking, turning, drawing, etc.
▰ It
performs tasks assigned to it.
▰ Task
is performed by choosing appropriate actions that need to be taken to solve the
task.
▰ It
selects the external actions from the environment through sensors.
Components of Performance
Element:
▰ It
performs 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
▰ It
responsible for making improvements, takes knowledge about performance element
and some feedback determines how to modify performance elements.
▰ It
takes in feedback from the critic and modifies the performance element
accordingly.
▰ It
is the main Component, it acquires knowledge, make some changes or improvements
to the system depending on the performance.
▰ It
is responsible for making improvements by observing the performance and will
try to make changes based on the performance.
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?
3. Critic:
▰ It
provides the learning element with information on how well the agent is
doing based on a fixed performance standard. E.g., the audience
▰ It
gives feedback to the Learner System or Learner Components
▰ It
determines the outcome of the performance elements or actions.
▰ It
determines the outcomes of the actions and give feedback to the learning
element.
▰ It
determines the outcomes of the action with respect to the fixed Performance
standards.
Type of Feedback
▻ Supervised
learning: correct answers for each example
▻ Unsupervised
learning: correct answers not given
▻ Reinforcement
learning: occasional rewards
4. Problem Generator:
▰ This
component suggests problems or actions that will generate new examples or the experience that helps the system to train further.
▰ It
provides the performance element with suggestions on new actions to take.
▰ It
suggests problem or actions that would lead to the generation of new examples to
improve learning.
▰ It
only suggests the new cause of actions, new ideas, situations to perform some
actions.
▰ It
is the main component that will make the agent keep on learning.
5. Sensors:
▰ It
gives Input from the environment
6. Actuators or Effectors:
▰ It
is used to carry out actions in the environment.
Learning Agent:
▰ To
predictor decides the resulting state for action.
▰ To
know the values for each state (understand which state has high or low value).
▰ To
keep a record of relevant percepts.
▰ Example
of Learning and its Components:
Internal Exam
of Student
▰ Performance
Element: Student is the Performance Element, Teacher guides you to perform
better and to make changes in your result to give better performance, you use
Knowledge and guidance are given by the teacher.
▰ Learning
Element: The teacher is a Learning Element, who checks the test and does marking.
On the basis of your performance, the teacher will guide you, what are the areas
where you need to make efforts to give better performance.
▰ Critic:
Test as Internal Exam students appears for. You will be having some
Performance Standards, which you have to match to be graded pass or fail or top
grades
▰ Problem
Generator: New ideas get generated on the basis of suggestions given to you
in the form of feedback for betterment in your performance. It generates new
ideas to be implemented and generated new ways to per from better.
Dr. Arpana Chaturvedi
Asst. Professor JIMS
Department of Information Technology
very informative 🔥🔥
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