Reasoning in AI: Best Explained 007ET

Reasoning in AI is deemed critical to enable effective artificial Intelligence programs to tackle complex decision-making problems using machine learning.

What is Reasoning in AI

Reasoning is the mental process of deriving logical conclusions and making predictions from available knowledge, facts, and beliefs. Or we can say, “Reasoning is a way to infer facts from existing data.” It is a general process of reasoning to find valid conclusions.

In artificial intelligence, reasoning is essential so that the machine can also reason like a human brain and perform like a human.

Types of Reasoning in Artificial Intelligence

In artificial intelligence, reasoning can be divided into the following categories:

  • Deductive reasoning
  • Inductive reasoning
  • Abductive reasoning
  • Common Sense Reasoning
  • Monotonic Reasoning
  • Non-monotonic Reasoning

Note: Inductive and deductive reasoning are the forms of propositional logic.

1. Deductive Reasoning in AI

Deductive Reasoning Definition

Deductive reasoning is deducing new information from logically related available information. It is the form of valid reason, which means the argument’s conclusion must be correct when the premises are true.

Deductive reasoning is a type of propositional logic in AI, and it requires various rules and facts. It is sometimes referred to as top-down reasoning and contradictory to inductive reasoning.

  • In deductive reasoning, the truth of the premises guarantees the truth of the conclusion.
  • Deductive reasoning mostly starts from the general premises to the specific conclusion, which can be explained below example.

Deductive Reasoning Examples

Premise-1: All the human eats veggies

Premise-2: Suresh is human.

Conclusion: Suresh eats veggies.

The general process of deductive reasoning is given below:

Deductive Reasoning Examples
Deductive Reasoning Examples

2. Inductive Reasoning in AI

Inductive Reasoning Definition

Inductive reasoning is a form of reasoning to arrive at a conclusion using limited sets of facts by generalization. It starts with a series of specific attributes or data and reaches a general statement or conclusion.

  • Inductive reasoning is a type of propositional logic, which is also known as cause-effect reasoning or bottom-up reasoning.
  • In inductive reasoning, we use historical data or various premises to generate a generic rule, for which premises support the conclusion.
  • In inductive reasoning, premises provide probable supports to the conclusion, so the truth of premises does not guarantee the truth of the conclusion.

Inductive Reasoning Examples

Premise: All of the pigeons we have seen in the zoo are white.

Conclusion: Therefore, we can expect all the pigeons to be white.

Reasoning in Artificial intelligence

3. Abductive reasoning in AI

Abductive Reasoning Definition

Abductive reasoning is a form of logical reasoning which starts with single or multiple observations then seeks to find the most likely explanation or conclusion for the statement.

Abductive reasoning is an extension of deductive reasoning, but the premises do not guarantee the conclusion in abductive reasoning.

Abductive Reasoning Examples

Implication: Cricket ground is wet if it is raining

Axiom: Cricket ground is wet.

Conclusion It is raining.

4. Common Sense Reasoning in AI

Common Sense Reasoning Definition

  • Common sense reasoning is an informal form of reasoning, which can be gained through experiences.
  • Common Sense reasoning simulates the human ability to make presumptions about events that occur every day.
  • It relies on good judgment rather than exact logic and operates on heuristic knowledge and heuristic rules.

Common Sense Reasoning Examples

  1. One person can be at one place at a time.
  2. If I put my hand in a fire, then it will burn.

The above two statements are examples of common sense reasoning which a human mind can easily understand and assume.

5. Monotonic Reasoning in AI

Monotonic Reasoning Definition

In monotonic reasoning, once the conclusion is taken, it will remain the same even if we add some other information to our knowledge base. In monotonic reasoning, adding knowledge does not decrease the set of prepositions that can be derived.

  • To solve monotonic problems, we can derive a valid conclusion from the available facts only, and it will not be affected by new facts.
  • Monotonic reasoning is not useful for real-time systems, as in real-time, facts get changed, so we cannot use monotonic reasoning.
  • Monotonic reasoning is used in conventional reasoning systems, and a logic-based system is monotonic.
  • Any theorem proving is an example of monotonic reasoning.

Monotonic Reasoning Examples

  • Earth revolves around the Sun.

It is a fact, and it cannot be changed even if we add another sentence in the knowledge base like, “The moon revolves around the earth,” Or “Earth is not round,” etc.

Advantages of Monotonic Reasoning:

  • In monotonic reasoning, each old proof will always remain valid.
  • If we deduce some facts from available facts, then it will remain valid for always.

Disadvantages of Monotonic Reasoning:

  • We cannot represent the real world scenarios using Monotonic reasoning.
  • Hypothesis knowledge cannot be expressed with monotonic reasoning, which means facts should be true.
  • Since we can only derive conclusions from the old proofs, so new knowledge from the real world cannot be added.

6. Non-monotonic Reasoning in AI

Non-monotonic Reasoning Definition

In Non-monotonic reasoning, some conclusions may be invalidated if we add more information to our knowledge base.

  • Logic will be said as non-monotonic if some conclusions can be invalidated by adding more knowledge into our knowledge base.
  • Non-monotonic reasoning deals with incomplete and uncertain models.
  • “Human perceptions for various things in daily life, “is a general example of non-monotonic reasoning.

Non-monotonic Reasoning Examples 

Let suppose the knowledge base contains the following knowledge:

  • Birds can fly
  • Penguins cannot fly
  • Pitty is a bird

So from the above sentences, we can conclude that Pitty can fly.

However, if we add another sentence into the knowledge base, “Pitty is a penguin,” which concludes “Pitty cannot fly,” it invalidates the above conclusion.

Advantages of Non-monotonic Reasoning

  • For real-world systems such as Robot navigation, we can use non-monotonic reasoning.
  • In Non-monotonic reasoning, we can choose probabilistic facts or can make assumptions.

Disadvantages of Non-monotonic Reasoning

  • In non-monotonic reasoning, the old facts may be invalidated by adding new sentences.
  • It cannot be used for theorem proving.

Difference between Inductive and Deductive Reasoning in AI

Reasoning in artificial intelligence has two basic forms, Inductive reasoning, and Deductive reasoning. Both reasoning forms have premises and conclusions, but both reasons are contradictory to each other. Following is a list for comparison between inductive and deductive reasoning:

While defining inductive reasoning, it is imperative to mention deductive reasoning. They are a basic form of propositional logic and standard techniques for reasoning in Artificial Intelligence, Machine Learning, and more. However, the similarities between inductive and deductive reasoning end here, as they follow contrasting processes and reach a conclusion with different levels of accuracy.

Therefore, here are some essential deductive and inductive arguments that differentiate these two reasoning techniques from one another:

  • Deductive reasoning uses available facts, information, or knowledge to deduce a valid conclusion, whereas inductive reasoning involves making a generalization from specific facts, and observations.
  • Deductive reasoning uses a top-down approach, whereas inductive reasoning uses a bottom-up approach.
  • Deductive reasoning moves from generalized statement to a valid conclusion, whereas Inductive reasoning moves from specific observation to a generalization.
  • In deductive reasoning, the conclusions are certain, whereas, in Inductive reasoning, the conclusions are probabilistic.
  • Deductive arguments can be valid or invalid, which means if premises are true, the conclusion must be true, whereas inductive argument can be strong or weak, which means conclusion may be false even if premises are true.

The differences between inductive and deductive can be explained using the below diagram based on arguments:

Difference between Inductive and Deductive Reasoning in AI
Difference between Inductive and Deductive Reasoning in AI

Comparison Chart: Inductive and Deductive Reasoning in AI

Basis for comparisonDeductive ReasoningInductive Reasoning
DefinitionDeductive reasoning is valid reasoning to deduce new information or conclusions from known related facts and information.Inductive reasoning arrives at a conclusion by the process of generalization using specific facts or data.
ApproachDeductive reasoning follows a top-down approach.Inductive reasoning follows a bottom-up approach.
Starts fromDeductive reasoning starts from Premises.Inductive reasoning starts from the Conclusion.
ValidityIn deductive reasoning, the conclusion must be proper if the premises are true.In inductive reasoning, the truth of premises does not guarantee the validity of conclusions.
UsageThe use of deductive reasoning is complex, as we need facts that must be true.The use of inductive reasoning is fast and easy, as we need evidence instead of facts. We often use it in our daily life.
ProcessTheory→ hypothesis→ patterns→confirmation.Observations-→patterns→hypothesis→Theory.
ArgumentIn deductive reasoning, arguments may be valid or invalid.In inductive reasoning, arguments may be weak or strong.
StructureDeductive reasoning reaches from general facts to specific facts.Inductive reasoning reaches from specific facts to general facts.
Inductive and Deductive Reasoning in AI

Summary Inductive and Deductive Reasoning in AI

Inductive Reasoning in AI

  • Makes a generalization from specific facts and observations.
  • In inductive reasoning, the argument’s premises can never guarantee that the conclusion must be true.
  • As it requires only evidence, its reasoning process is fast and easy.
  • Here the process moves from specific to general.
  • Also known as cause-effect or bottom-up reasoning.

Deductive Reasoning in AI

  • Reaches a valid conclusion using available facts and knowledge.
  • In deductive reasoning, an argument is valid when, assuming the premises are true, the conclusion must be true.
  • As it requires true facts, its process is difficult in comparison to inductive’s.
  • Whereas, here the process moves from general to specific.
  • Also known as top-down reasoning

Conclusion Reasoning in AI

With the complexities and difficulties in computer science, artificial intelligence, and mathematics increasing day by day, the need for machines capable of performing reasoning like humans is growing tremendously.

AI experts worldwide are leveraging these reasoning techniques and their capabilities to teach robots and machines with remarkable reasoning abilities that will allow them to solve complex problems and reach the most suitable solution and conclusion using their full potential.

Hence, there is no wonder that in the upcoming years, these reasoning techniques, along with other advanced technologies, will give way to a technology that could effortlessly outperform the reasoning performed by the human brain.

FAQ – Reasoning in AI

What are the 4 types of reasoning?

There are four primary forms of logic: deductive, inductive, abductive, and metaphoric inference.

What is reasoning and types of reason?


The reasoning is the process of using existing knowledge to conclude, make predictions, or construct explanations. Three methods of reasoning are the deductive, inductive, and abductive approaches. Deductive reasoning: conclusion guaranteed.

What is knowledge and reasoning in AI?

Knowledge representation and reasoning (KR, KRR) is the part of Artificial intelligence that is concerned with AI agents thinking and how thinking contributes to the intelligent behavior of agents. … It is also a way that describes how we can represent knowledge in artificial intelligence.

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