Reasoning
Reasoning is the mental process of drawing logical conclusions and forecasts from the knowledge, facts, and beliefs that are at hand. Alternately, we could assert that "reasoning is a means to deduce facts from available data. Finding sound conclusions involves using reasonable thought in general. In artificial intelligence, reasoning is crucial for the computer to be able to function and think rationally like a human brain.
TYPES OF Reasoning:-
Ø Deductive reasoning
Ø Inductive reasoning
Ø Abductive reasoning
Ø Common Sense Reasoning
Ø Monotonic Reasoning
Ø Non-monotonic Reasoning
1.
Deductive
reasoning:
Deductive reasoning is the process of
inferring new knowledge from previously known data that is logically connected.
Because it is a valid method of reasoning, when the premises are true, the
conclusion of the argument must also be true.
In AI, deductive reasoning is a form of
propositional logic that calls for a variety of facts and rules. Top-down
reasoning is another name for it, and it runs counter to inductive reasoning.
The validity of the premises ensures the
validity of the conclusion in deductive reasoning.
Deductive reasoning mostly starts from
the general premises to the specific conclusion, which can be explained as
below example.
Example:
Premise-1: All the human eats veggies
Premise-2: Suresh is human.
Conclusion: Suresh eats veggies.
2. Inductive Reasoning: Using a small number of data and the generalisation process,
inductive reasoning is a type of reasoning that leads to a conclusion. Starting
with a collection of specific facts or evidence, it progresses to a broad
assertion or conclusion.
Cause-and-effect reasoning, commonly
referred to as bottom-up reasoning or propositional logic, is a subset of
inductive reasoning.
When using inductive reasoning, we
create a general rule that is supported by the premises by using historical
evidence or a variety of premises.
The truth of the premises does not
ensure the truth of the conclusion in inductive reasoning because the premises
offer possible justifications for the conclusion.
Example:
The zoo's pigeons are all white, as we
have observed.
Therefore, we may anticipate that all
3. Abductive reasoning: Abductive reasoning is a type of logical reasoning that
begins with a single or a series of observations before attempting to identify
the most logical conclusion or explanation for the observation.
The premises do not always guarantee the
conclusion in abductive reasoning, which is an extension of deductive
reasoning.
Example:
Conclusion: If it is raining, the
cricket field is moist.
The cricket field is soggy.
Conclusion Rain is falling.
4. Use of Common Sense
An informal type of thinking that can be
acquired through experience is common sense reasoning.
The ability to form assumptions about
everyday events is simulated by common sense thinking.
It is based on heuristic knowledge and
heuristic principles and depends on good judgement rather than precise
reasoning.
Example:
A single person can be in just one place at
once. My hand will burn if I put it near a fire. The two statements mentioned
above are examples of common sense thinking that a human mind can readily
comprehend and make assumptions about.
5. Monotonic Reasoning: When monotonic reasoning is used, the conclusion reached will
remain unchanged even if additional information is added to the already-present
data in our knowledge base. The number of prepositions that can be inferred
does not go less in monotonic reasoning when more information is acquired.
We can only draw a valid conclusion from
the information that is already accessible in order to tackle monotonic
problems; fresh information will have no bearing on it.
Due to the fact that facts might change
in real time, monotonic reasoning is not appropriate for real-time systems.
A logic-based system is monotonic, and
typical reasoning systems use this type of reasoning.
Monotonic reasoning can be shown in any
theorem proof.
Example:
The Sun orbits the Earth.
Even if we add a new fact to the
knowledge base, such as "The moon rotates around the earth" or
"Earth is not round," it won't change the fact that it is true.
Advantages :-
The benefits of monotonic reasoning
include the constant validity of each previous proof.
If we infer some facts from the
information at hand, they will always be true.
Disadvantage :-
We cannot depict real-world
circumstances with monotonic reasoning, which is one of its drawbacks.
Facts should be true because hypothesis
knowledge cannot be conveyed using monotonic reasoning.
Since we are limited to drawing
inferences from prior evidence, fresh information from the real world cannot be
contributed.
6. Reasoning that is not monolithic
If we expand our knowledge base, certain
conclusions drawn through non-monotonic reasoning may become invalid.
If certain conclusions can be refuted by
gaining additional knowledge, logic is said to be non-monotonic.
Non-monotonic reasoning takes into
account faulty and ambiguous models.
A general illustration of non-monotonic
reasoning is "Human perceptions for numerous things in daily life.
Let's use the following knowledge as an
example from the knowledge base:
Birds have wings.
No penguins can fly.
A bird named Pitty
We can therefore infer that Pitty can
fly from the sentences above.
The preceding conclusion is invalidated,
though, if we add the additional line "Pitty is a penguin," which
follows from the fact that "Pitty cannot fly."
Benefits of non-monotonic
thinking: We can apply non-monotonic
reasoning for real-world systems like robot navigation.
We have the option to select
probabilistic facts or create assumptions when using non-monotonic reasoning.
Non-monotonic Reasoning
Drawbacks: In non-monotonic reasoning,
the old facts may be rendered invalid by the addition of new phrases.
It cannot be used to prove theorems.
Deductive reasoning differs
from inductive reasoning.
Inductive reasoning and deductive
reasoning are the two main types of reasoning used in artificial intelligence.
Although both kinds of thinking have premises and conclusions, they are
incompatible with one another. The list below can be used to contrast inductive
and deductive reasoning:
In contrast to inductive reasoning,
which involves drawing broad conclusions from specific facts and observations,
deductive reasoning draws conclusions from facts, information, or knowledge
that is already known.
Inductive reasoning follows a bottom-up
process, whereas deductive reasoning follows a top-down process.
Inductive reasoning proceeds from a
single observation to a generalisation, whereas deductive reasoning moves from
a generalised assertion to a reliable conclusion.
Inductive reasoning yields probabilistic
findings, while deductive reasoning yields conclusions that are certain.
Deductive inferences may be true or
false,
0 Comments