Types of Reasoning

- Overview

Reasoning in AI is the process of using existing knowledge to make informed decisions or derive new information. It's a key component of AI applications like natural language processing, machine learning, and expert systems. 

Reasoning in AI involves using prior knowledge to:

• Make inferences
• Form hypotheses
• Develop strategies for addressing a problem
• Draw conclusions
• Solve problems

Reasoning in AI aims to create machines that can reason like humans, using logic, common sense, and intuition. 

Reasoning is a complex process that involves many different AI techniques. Some examples of reasoning in AI include:

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

Each type of reasoning listed above has advantages and disadvantages depending on the task to which it is applied. Advancing the possibilities of artificial intelligence by understanding all of the major types of artificial intelligence reasoning below can bring us closer to more useful and powerful general artificial intelligence. 

However, whether this is truly the optimal goal for the future state of AI remains to be seen, but it suggests that AI capable of reasoning at this level of complexity will be a game changer.

- Deductive Reasoning

Deductive Reasoning is the strategic approach that uses available facts, information or knowledge to draw valid conclusions. It basically beliefs in the facts and ideas before drawing any result. Deductive reasoning uses a top-down approach. 

In deductive reasoning, the arguments can be valid or invalid based on the value of the premises. If the value of the premises is true, then the conclusion is also true. Deductive reasoning helps in scanning the generalized statement into a valid conclusion. 

Some of the examples are

• People who are aged 20 or above are active users of the internet.
• Out of the total number of students present in the class, the ratio of boys is more than the girls.

- Inductive Reasoning

Inductive reasoning is completely different from the deductive reasoning approach because Inductive reasoning is associated with the hypothesis-generating approach rather than drawing any particular conclusion to the facts at the beginning of the process. 

Inductive reasoning help in making generalization from specific facts and knowledge. Inductive reasoning is the bottom-up process. In inductive Reasoning even if the premises are true there is no chance that the conclusion will also be true because it depends upon the inductive argument which can be either strong or weak. 

Some of the examples are:

• All the students present in the classroom are from London.
• Always the hottest temperature is recorded in Death Valley.

- Common Sense Reasoning

Common sense reasoning is the most occurred type of reasoning in daily life events. It is the type of reasoning which comes from experiences. 

When a human face a different situation in life it gain some knowledge.So whenever in the next point of time it faces a similar type of situation then it uses its previous experiences to draw a conclusion to do situation. 

Some of the examples are:

• when a bike crosses the traffic signal when it is red then it learns from its mistakes and next time the bike is aware of the signal and actions.
• While overtaking someone on the road what all ideas should be kept in mind.

- Monotonic Reasoning

It is the type of reasoning which follows a different approach towards the thinking process it uses facts, information and knowledge to draw a conclusion about the problem but the major point is its conclusion remain fixed permanently once it is decided because even if we add new information or facts to the existing one the conclusion remains the same it doesn’t change. 

Monotonic reasoning is used mainly in conventional reasoning systems and logic-based systems. 

Some Examples of monotonic are:

• The Sahara desert of the world is one of the most spectacular deserts.
• One of the longest rivers in the world is the Nile River.

- Abductive Reasoning

Abductive Reasoning is a type of reasoning which acts differently from all the above reasoning strategies. It begins with an incomplete set of facts, information and knowledge and then proceeds to find the most deserving explanation and conclusion. 

It draws conclusions based on what facts you know at present rather than collecting some outdated facts and information. It mostly plays a great role in the daily life decision-making process. 

Some of the examples are:

• Doctor drawing conclusions regarding your health based on test reports. 
• A bowl of soup is kept and vapour evaporating from it which draws the conclusion that the bowl is hot in nature.

- Non-Monotonic Reasoning

Non-monotonic reasoning is a subfield of AI that deals with incomplete and uncertain models. It involves reasoning systems that can jump to conclusions that may be retracted later when new information becomes available. 

In non-monotonic reasoning, some conclusions may be invalidated if more information is added to the knowledge base. It's useful for representing defaults, which are rules that can be used unless they are overridden by an exception. 

Non-monotonic reasoning is a property of non-classical reasoning, which is commonly seen in commonsense reasoning. For example, the likely explanation for seeing wet grass is that it rained. However, this explanation has to be retracted when learning that the real cause was a sprinkler.