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Artificial intelligence (AI) has recently taken on a life of its own in our daily lives, personally and professionally. What sets AI apart from other software and technologies is its capacity to mimic and learn human-like behaviours.

Robots and AI are no longer the stuff of science fiction, as we can now say with confidence. AI is now inevitable in our lives, whether at work or home, and it is quickly overtaking mobile devices in terms of popularity.

Increasingly, AI is stepping up to assist us in ways that only a human could. When one thinks of AI, the first thing that comes to mind is the idea of self-driving cars navigating complex road situations, and this is something that researchers can accomplish with the help of Machine Learning and Deep Learning. Nonetheless, the most critical factor here is whether AI is as intelligent as we believe it is.

Reasoning in AI:

AI requires reasoning to reason and perform similarly to a human brain.

Types of Reasoning

Deductive reasoning:

Deductive reasoning is inferring new information from previously known data connected logically. The argument's conclusion must be proper if and only if the premises are true. Moreover, in AI, deductive reasoning is a propositional logic requiring many rules and facts. It's also known as top-down reasoning, and it's the opposite of inductive reasoning.

Inductive reasoning:

Inductive reasoning uses the process of generalisation to arrive at a conclusion using a limited set of facts. It begins with specific facts or data and a broad statement or conclusion. Additionally, Inductive reasoning, also known as cause-effect reasoning or bottom-up reasoning, is a type of propositional logic. Moreover, Inductive reasoning relies on prior knowledge or a variety of premises to arrive at a general rule.

Abductive reasoning:

If you've made single or multiple observations, you can use abductive reasoning to determine the most likely explanation or conclusion. Unlike deductive reasoning, the premises in abductive reasoning do not lead to a conclusion.

Common Sense Reasoning:

To reason with common sense is a skill acquired through life's experiences. Furthermore, It is based on heuristic knowledge and heuristic rules rather than on exact logic.

Monotonic Reasoning:

Even if we add new information to our knowledge base, our conclusion will remain the same if we use monotonic reasoning. The number of prepositions derived from further information does not change monotonic reasoning. To solve monotonic problems, we can only draw a valid conclusion from the current facts, and new facts won't change it. Traditional reasoning employs monotonic reasoning, and a logic-based system is also monotonic.

Non-monotonic Reasoning:

Non-monotonic reasoning allows for the possibility that new information may invalidate previously reached conclusions. Moreover, the logic is non-monotonic if machines can disprove conclusions by acquiring new information. Furthermore, complicated and uncertain models are the focus of non-monotonic reasoning.

Problem-solving in AI:

AI methods of problem-solving are either specific or general. A special-purpose method is designed for a specific problem and often takes advantage of the unique situation. On the other hand, researchers can use a general-purpose method to solve a wide range of issues. The means-end analysis is a common AI technique for incrementally reducing the current and desired states gap. For example, a robot that can pick up, put down, move forward, back, and to the left and right, the programme selects from a list of possible actions.

Conclusion:

The reasoning is the AI Machine's main weakness compared to human intelligence. The machines are more than capable of providing feedback and answering questions; however, they cannot explain how they arrived at their conclusions.

Alan Turing addressed the question "Can machines think?" is the most famous paper (published in 1950). The invention of the "imitation game" is still used to interpret machine intelligence today. Even after seventy years, the answer and results of the experiment remain the same: "not a single AI system...has passed the Turing Test," which is quite perplexing in today's world.