Reasoning in Artificial Intelligence

What is Reasoning?

Artificial intelligence (AI) systems use reasoning to analyze data, conclude, and take action depending on the information they access. The ability of robots to simulate cognitive functions similar to those of humans, allowing them to solve problems, learn from mistakes, and successfully interact with their environment, is a critical component of AI. Reasoning may take place at varying levels of complexity and be divided into numerous types:

There are numerous kinds of reasoning, each with its own goals and ways of concluding. The main categories of reasoning consist of the following:

1. Deductive Reasoning

Drawing particular conclusions from broad premises or principles is a process known as deductive reasoning. It is a style of reasoning where the conclusion logically follows from the stated premises or claims. In deductive reasoning, if the premises are true, the conclusion must also be proper. It is a fundamental type of logical inference frequently applied in formal systems, logic, and mathematics.

Here is an illustration of deductive reasoning:

First premise: People are mortal.

Second premise: John is a person.

John is mortal, therefore.

The first assumption in this illustration states the universal truth that all people are mortal. The second premise states that John is a person and gives details about him. The conclusion follows from the supplied information is established by integrating these two premises: John is mortal.

• In formal logic and mathematical proofs, where conclusions are obtained piecemeal from axioms and rules of inference, deductive reasoning is frequently utilized. As long as the premises are accurate and the logical rules are sound, it is regarded as a dependable style of reasoning. However, even if the deduction method is sound, the conclusion could only be correct if the premises are correct or the reasoning is correct.

2. Inductive Reasoning

When using inductive reasoning, generalizations or predictions are drawn based on particular observations or pieces of data. It entails progressing from specific cases or facts to more general conclusions. In contrast to deductive reasoning, inductive reasoning delivers plausible or likely conclusions based on the data at hand but does not ensure that the conclusions are accurate.

The following steps are often included in the inductive reasoning process:

• Observation: Compiling precise information and proof through observations or experiments.
• Pattern recognition: Pattern recognition is known as finding patterns, trends, or regularities in the observed data.
• Formulation of Hypothesis: A tentative explanation or hypothesis explaining the observed data should be formulated based on the patterns seen.
• Prediction: Making predictions based on the hypothesis regarding recent or upcoming observations or occurrences.
• Testing and Refinement: Continuously evaluating the hypothesis in light of fresh information and improving it in light of new evidence.

Example

• Observation: You notice that you have an allergic response every time you consume a certain kind of food.
• Pattern Recognition: You see a pattern wherein the allergenic food regularly causes an allergic reaction in you.
• Hypothesis: You come up with the theory that you are allergic to that food.
• Prediction: Using your hypothesis as a guide, you make the following prediction: You will develop an allergy if you consume that meal again.
• Testing and Improvement: You try eating the food again to test your theory; if you do and have an allergic response, your theory is supported. You might need to change or improve your hypothesis if the reaction is not seen.

3. Abductive Reasoning

Being a detective while using abductive reasoning. When something uncommon or unexpected is observed, you try to come up with various ideas about why it may have occurred. Then, you evaluate which answer makes the most sense based on the information you already have and the available evidence. Finally, depending on the information you know, you select the most plausible explanation.

For instance, if your favorite food is missing when you get home, you may consider:

• Perhaps you ate it and then forgot.
• Perhaps a different household member ate it.
• Perhaps a pet found it.
• Perhaps a thief broke in and snatched it.

To determine what is most likely to have occurred, you would weigh all of the possible outcomes and the available information. Finding the best explanation based on the available information is an example of abductive reasoning.

4. Common sense Reasoning

The capacity of AI systems to comprehend and use the common-sense information and reasoning that humans possess naturally is referred to as common-sense reasoning. Even without specific facts, it entails assuming the best and generating judgments based on a general world understanding. A key component of human intelligence is common sense thinking, which enables us to successfully navigate the environment, comprehend natural language, and communicate with others.

For instance, AI and Hot Coffee

Consider an AI system that is created to help people with routine activities. "Can you please get me a cup of coffee from the kitchen?" a user asks the AI one day.

This request may be taken literally by a system devoid of common-sense thinking, in which case it would retrieve a cup of coffee from the kitchen whether it was hot or cold. However, using logic and common sense, the AI system deduces that the user wants a hot cup of coffee and that coffee is usually served hot.

5. Monotonic Reasoning

In a type of logical reasoning known as monotonic reasoning, additional data or knowledge added to a set of premises never renders earlier inferences erroneous. In other words, as more information is provided, the conclusions drawn using monotonic reasoning stay valid. This school of thought operates under the presumption that new information can only bolster already-held views and conclusions.

A good illustration of monotonic thinking is:

To explain monotonic thinking, let's look at a straightforward example:

First premise: All cats have tails.

Second premise: Fluffy is a cat.

Consequently, Fluffy has a tail.

Conclusion: "Fluffy has a tail" is still valid in monotonic reasoning, even if we provide additional details.

Premise 3: Fluffy is a household cat, as an illustration, suppose we add another premise.

6. Non-monotonic Reasoning

A type of logical reasoning known as non-monotonic reasoning allows for the retraction or modification of findings in response to new information or learned knowledge. Non-monotonic reasoning, in contrast to monotonic reasoning, acknowledges that new information or premises may need reevaluating previously reached conclusions. This sort of reasoning works well when dealing with ambiguity, partial knowledge, and circumstances where the available information may alter or be updated over time.

Non-monotonic reasoning as an example:

As an illustration of non-monotonic thinking, let's utilize the identical case from earlier:

Premise 1: All cats have tails.

Premise 2: A cat is named Fluffy.

Accordingly, Fluffy has a tail.

Adding a fresh example now

3. Fluffy is a Manx breed cat distinguished by its lack of a tail.

Advantages

• Intelligent Decision-Making: Using reasoning, AI systems can analyze data, weigh various options, and make wise choices based on facts and knowledge. For activities that need intellect and critical thought, this capacity is essential.
• Problem-solving: AI systems utilize reasoning to find patterns, establish connections, and deduce answers to challenging issues. AI may use reasoning to deconstruct issues into their parts and use the best problem-solving techniques.
• Learning and Adaptation:  Using reasoning, AI systems may take lessons from the past and adjust to novel circumstances. By incorporating criticism and learning from their failures, they may gradually hone their knowledge and enhance their performance.
• Natural Language Processing: Reasoning is crucial for comprehending natural language, allowing AI systems to converse with one another, grasp and respond to human language, and derive meaning from text.
• Human-Like interaction: Reasoning-based AI systems can engage with people more intelligently, comprehending context, managing difficult questions, and giving more customized and pertinent answers.

Disadvantages

• Uncertainty: Ambiguity and uncertainty are standard components of real-world issues. When faced with ambiguity, reasoning might result in erroneous conclusions or unexpected results.
• Lack of data: AI systems frequently deal with missing or insufficient information. Reasoning with little data might provide erroneous results or make it more difficult to draw reliable conclusions.
• Lack of common sense: AI reasoning systems may need help using common sense, which people utilize naturally to make daily decisions. Making AI thinking consistent with common sense still needs to be more accessible.
• Scalability: AI systems may find it difficult to scale effectively as the complexity of reasoning tasks rises. A system's computing and memory needs may increase exponentially when more rules or information are added.
• Overfitting: AI reasoning models may get too tuned to specific patterns in the training data, which impairs their ability to generalize to new and unexplored material.