rote learning

1. What is learning-
The action of receiving instruction or acquiring knowledge’
‘A process which leads to the modification of behaviour or the
acquisition of new abilities or responses, and which is additional to
natural development by growth or maturation’
Learning denotes changes in a system that enable the system to do the same
task more efficiently next time.
Learning is an important feature of intelligence".
Types of learning
Rote Learning:
 Rote Learningrefers to Learning by memorization;
 One-to-one mapping from inputstostored representation;
 Association-based storageandretrieval.
 Saving knowledge so it can be used again.
 Retrieval is the only problem.
 No repeated computation, inference or query is necessary.

2. Rote learningavoidsunderstanding
It avoids understanding the inner complexities but focuses on memorizing the
materialso that it can be recalled by the learner exactly the way it was
read or heard.
 Rotelearningis-Learning by Memorization
Learning by memorizations is the simplest from of learning. It requires the least
amount of inference and is accomplished by simply copying the knowledge in
the same form that it will be used directly into the knowledge base.
Example:- Memorizing multiplication tables, formulate , etc.
 RotelearningisLearning something by Repeating
Repeatingover and over and over again;saying the same thing and trying
to remember how to say it; itdoes not help us to understand; it helps us to
remember, like we learn a poem, or a song, or something like that by rote
learning.
A simple example of rote learning is caching
 Store computed values (or large piece of data)
 Recall this information when required by computation.
 Significant time savings can be achieved.
 Many AI programs (as well as more general ones) have used caching very
effectively.

3. Memorization is a key necessity for learning:It is a basic necessity for any
intelligent program -- is it a separate learning process?Memorization can be a
complex subject -- how best to store knowledge?
 Samuel's Checkers program employed rote learning
 A minimax search was used to explore the game tree.
 Time constraints do not permit complete searches.
 It records board positions and scores at search ends.
 Now if the same board position arises later in the game the stored value can be
recalled and the end effect is that deeper searched have occurred.
Rote learning is basically a simple process. However it does illustrate some issues that
are relevant to more complex learning issues.
Organisation
-- access of the stored value must be faster than it would be to recompute it. Methods
such as hashing, indexing and sorting can be employed to enable this.
E.g Samuel's program indexed board positions by noting the number of pieces.
Generalisation
-- The number of potentially stored objects can be very large. We may need to
generalise some information to make the problem manageable.
E.g Samuel's program stored game positions only for white to move. Also rotations
along diagonals are combined.
Stability of the Environment
-- Rote learning is not very effective in a rapidly changing environment. If the
environment does change then we must detect and record exactly what has changed --
the frame problem.
When is there a problem with rote learning?
When rote memorization is applied as the main focus of learning, it is not considered
higher-level thought or critical thinking. Opponents to rote memorization argue that
creativity in students is stunted and suppressed, and students do not learn how to think,
analyze or solve problems. These educators believe, instead, that a more associative or
constructive learning should be applied in the classroom. If the majority of the student’s
day is spent on repetition, the foundation for learning becomes shaky.
Oftentimes, teachers are scorned for “teaching to the test,” referring to standardized
testing, and are criticized for applying rote memorization as a foundational skill. When the

4. role of rote memorization is an end in itself, instead of a means to an end, rote
memorization fails as a building block to critical thinking.
When the argument focuses on rote learning as an either/or situation, rote learning is
stigmatized as a technique that “lazy” or “uninformed” teachers use. But, in reality, rote
learning and higher-level thinking are actually intimately intertwined.
When—and why—is rote learning is useful?
As an alternative approach to subject areas that require memorization with disdain and
conflict, teachers can build higher-level critical thinking skills with rote learning as the
foundation.
Rote learning as a building block
Consider this: How do students learn the alphabet or multiplication tables if not through
rote memorization? For that matter, can a high school chemistry student progress without
having the Table of Elements memorized?
While it’s not a means to an end, rote learning is necessary if you want to engage in
higher-level thinking. After all, can you do calculus or engineering-math, or even basic
algebra, if you constantly have to remember how to multiply or look up functions and
operations? That method would take forever. And you won’t likely have “ah-ha” moments
or breakthroughs.
As another example, these same principles also applies to spelling. Although everyone
today uses word processors with spell check, spelling is still important when filling out
forms and writing letters. Knowing how to spell makes writing easier and faster.
To truly engage in higher level thinking, students must first learn basic material and
memorize this material so they can refer to it later down the road when dealing with more
advanced lessons and learning.
Rote learning is not an either/or matter
Rote learning and memorization do not equal higher-level thinking, and should not
replace one for the other. Rote learning, however, is the cornerstone of higher-level
thinking and should not be ignored. Especially in today’s advanced technological world,
rote memorization might be even more important than ever! Think of rote learning as the

5. the filing system for your brain. If you can easily access the information when performing
a certain task, the brain is free to make major leaps in learning
.

6. Explanation-Based Learning (EBL)
In simple terms, it is the ability to gain basic problem-solving techniques by
observing and analyzing solutions to specific problems. In terms of Machine
Learning, it is an algorithm that aims to understand why an example is a part of
a particular concept to make generalizations or form concepts from training
examples. For example, EBL uses a domain theory and creates a program that
learns to play chess.
• An EBL system attempts to learn from a single example x by explaining why x is an example of
the target concept.
• The explanation is then generalized, and then system’s performance is improved through the
availability of this knowledge.
• We can think of EBL programs as accepting the following as input:
• i) A training example: what the learning model sees in the
world.
• ii)A goal concept: a high level description of what the model is
supposed to learn.
• iii) A operational criterion: states which other terms
• can appear in the generalized result.
• iv) A domain theory: set of rules that describe relationships
between objects and actions in a domain.
•
From the above 4 parameters, EBL uses the domain theory to find that training
example, that best describes the goal concept while abiding by the operational
criterion and keeping our justification as general as possible.
EBL involves 2 steps:
1. Explanation — The domain theory is used to eliminate all the
unimportant training example while retaining the important ones that best
describe the goal concept.
2. Generalization — The explanation of the goal concept is made as
general and widely applicable as possible. This ensures that all cases are
covered, not just certain specific ones.

7. EBL Architecture:
 EBL model during training
 During training, the model generalizes the training example in such
a way that all scenarios lead to the Goal Concept, not just in
specific cases. (As shown in Fig 1)
Fig 1 : Training EBL Model
• During the explanation step, the domain theory is used to prune away all
the unimportant aspects of the training example with respect to the goal
concept. What is left is an explanation of why the training example is an
instance of the goal concept. This explanation is expressed in terms that
satisfy the operationally criterion.
The next step is to generalize the explanation as far as possible while still
describing the goal concept.

8.  EBL model after training
 Post training, EBL model tends to directly reach the hypothesis
space involving the goal concept. (As shown in Fig 2)
Thus,EBL is abstracting a general concept from a particular training
example. EBL is a technique to formulate general concepts on the basis
of a specific training example. EBL analyses the specific training
example in terms of domain knowledge and the goal concept. The result
of EBL is an explanation structure, that explains why the training
example is an instance of the goal concept. The explanation-structure is
then used as the basis for formulating the general concept.