Artificial Intelligence is the design of machines that mimic human thoughts and actions, enabling applications such as expert systems, natural language processing, speech recognition, and computer vision. Reasoning is crucial in artificial intelligence, as it helps understand the human brain and its thought processes, enabling the creation of conclusions and predictions.

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. 

Deductive reasoning

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It's often contrasted with inductive reasoning, where you start with specific observations and form general conclusions. Deductive reasoning is also called deductive logic or top-down reasoning.

Inductive reasoning

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It's usually contrasted with deductive reasoning, where you go from general information to specific conclusions. Inductive reasoning is also called inductive logic or bottom-up reasoning.

Abductive reasoning

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 observation.

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

Analogical Reasoning

An analogy is a comparison between two objects, situations or events, that highlights respects in which they are thought to be similar.

Cause-and-effect reasoning

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.

Decompositional reasoning

Decompositional reasoning breaks things down into their smallest parts and assumes that the whole is the sum of the parts.

Monotonic Reasoning

In monotonic reasoning, once the conclusion is taken, then it will remain the same even if we add some other information to existing information in 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 the valid conclusion from the available facts only, and it will not be affected by new facts.

Monotonic reasoning is not useful for the 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.

Non-monotonic Reasoning

In Non-monotonic reasoning, some conclusions may be invalidated if we add some 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.

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