Reasoning in Artificial Intelligence
Artificial intelligence (AI) has gone a long way since its inception in 1956 and has become a vital part of our daily life. Today, diverse software and machines employ technology to conduct a variety of functions such as assessment, prediction, issue resolution, and more. Not only that, but businesses all over the world are investing in technology to make their operations faster, smarter, more competitive, and more efficient.
However, Artificial Intelligence relies on three key components to complete these tasks:
- Reasoning
- Interaction
- Learning
Each of these points intersects with the others, allowing artificial intelligence machines and systems to do jobs with human-like accuracy and speed. Today, we’ll look at one of these components, Reasoning, in order to better grasp its importance and role in Artificial Intelligence.
What Is Reasoning?
Reasoning is a type of inference and logical process that is conveyed through words (qualitative reasoning) and other symbols (quantitative reasoning) in such a way that the legitimacy of the process can be reviewed if necessary. It is associated with thinking, intellect, and cognition. Furthermore, it is a method of thinking that allows one to travel from one connected notion to another.
The reasoning is a natural human talent that entails receiving provided information, comparing it to what is already known, and then arriving at a conclusion. Due to its importance in making decisions, solving issues, and evaluating things, the reasoning is now widely employed in a variety of domains such as mathematics, logic, and artificial intelligence.
Reasoning In Artificial Intelligence
Artificial intelligence and its several subfields, such as machine learning, deep learning, natural language processing, and others, rely on reasoning and knowledge representation to assist machines to perform human-level operations. Artificial intelligence reasoning enables machines to think rationally and perform human-like functions.
It’s a key field of AI study that aids robots in problem-solving, deriving logical answers, and making predictions based on existing information, knowledge, facts, and data. Furthermore, depending on how machines handle uncertainty and partial facts, reasoning can be done in a formal or informal fashion, as well as top-down or bottom-up techniques. Artificial Intelligence techniques such as Probabilistic Reasoning allow machines to deal with and represent uncertain knowledge and information.
As a result, reasoning is one of the most essential characteristics associated with universal intelligence, whether human or artificial, because it allows both humans and machines to generate knowledge that was not before available.
Example Of Reasoning In Artificial Intelligence
Consider the following two examples to better understand how reasoning works in artificial intelligence applications and systems:
1. Alexa: is a cognitive virtual assistant that employs reasoning to make recommendations and suggestions based on orders. For instance, the closest place, the date for tomorrow, the AM and PM, and so on.
2. WolframAlpha: To do mathematical computations based on meal portions, this computational knowledge engine uses reasoning.
In brief, machines, like humans, use reasoning, knowledge representation, logic, and learning to analyze, solve problems, draw conclusions, and more.
Types Of Reasoning In Artificial Intelligence
There are various methods of reasoning in artificial intelligence, each of which is focused on inferring facts from existing data. These are the following:
1. Deductive reasoning
Deductive reasoning is a strategic technique for drawing logical conclusions based on existing facts, information, or knowledge. Before forming any conclusions, it fundamentally believes in the facts and concepts. Deductive reasoning employs a top-down approach.
The arguments in deductive reasoning might be legitimate or incorrect depending on the value of the premises. If the premises are true, then the conclusion must be true as well. Deductive reasoning aids in the transformation of a broad statement into a sound conclusion. Here are a few examples:
- Active internet users are those who are 20 years old or older.
- The ratio of boys to girls in the class is greater than the ratio of girls in the class.
2. Inductive Reasoning
Inductive reasoning is a type of reasoning that uses the process of generalization to arrive at a conclusion utilizing a restricted collection of information. It begins with a set of precise facts or data and ends with a broad assertion or conclusion.
We employ historical data or a collection of premises to come up with a general rule, the premises of which support the conclusion, in inductive reasoning.
The truth of premises does not ensure the truth of the conclusion in inductive reasoning because premises provide likely grounds for the conclusion.
Example:
- Premise: In the zoo, all of the pigeons we’ve seen are white.
- As a result, we can expect all of the pigeons to be white.
3. Common Sense Reasoning
The most common sort of reasoning in daily life occurrences is common sense reasoning. It’s the kind of reasoning that comes from personal experience. When a person is confronted with a new scenario in life, he or she gains knowledge. As a result, whenever it encounters a similar situation in the future, it will make a judgment based on its previous experiences. Some instances include:
- When a bike crosses a red traffic signal, it learns from its mistakes and is more conscious of the signal and its actions the next time.
- What should be kept in mind before overtaking someone on the road
4. Abductive Reasoning
Abductive Reasoning is a style of reasoning that differs from all of the previous tactics. It starts with an incomplete piece of facts, data, and knowledge before moving on to the best appropriate explanation and conclusion. Rather than gathering old data and information, it draws inferences based on what you know right now. It is primarily used in the decision-making process in daily life. Here are a few examples:
- Based on test results, your doctor draws inferences about your health.
- The presence of vapor evaporating from a bowl of soup leads to the conclusion that the bowl is hot in nature.
5. Monotonic Reasoning
In monotonic reasoning, once a conclusion is reached, it will remain unchanged even if new information is added to the existing knowledge base. The number of prepositions that can be deduced does not decrease when knowledge is added to a monotonic reasoning system.
We can get a correct conclusion from the available data alone to address monotone problems, and it will not be influenced by fresh facts.
For real-time systems, monotonic reasoning is unproductive because facts change in real-time, making monotonic reasoning worthless.
In traditional reasoning systems, monotonic reasoning is applied, and a logic-based system is monotonic. Monotonic reasoning can be used to prove any theorem.
Example:
- The Sun circles around the Earth.
It is a fact that cannot be changed, even if we add another sentence to our knowledge base, such as “The moon revolves around the earth” or “The Earth is not round,” and so on.
Monotonic Reasoning’s Benefits
- In monotonic reasoning, each old proof will always be valid.
- If we deduce some facts from existing facts, they will always be valid.
Monotonic Reasoning’s Drawbacks
- Monotonic reasoning cannot be used to represent real-world scenarios.
- Hypothesis knowledge cannot be conveyed using monotonic reasoning, hence facts must be correct.
- New knowledge from the real world cannot be added because we can only draw inferences from past proofs.
5. Non-monotonic Reasoning
Some findings in non-monotonic reasoning may be refuted if we add more information to our knowledge base.
If certain conclusions can be disproved by adding new knowledge to our knowledge base, logic is said to be non-monotonic.
Non-monotonic reasoning deals with models that are partial or uncertain.
A general example of non-monotonic thinking is “human perceptions of numerous things in daily life.”
Let’s say the knowledge base has the following information:
- Birds have the ability to fly.
- Penguins are unable to fly.
- Pitty is a type of bird.
As a result of the preceding words, we can deduce that Pitty can fly.
However, if we add another line to the knowledge base, such as “Pitty is a penguin,” the conclusion “Pitty cannot fly” is invalidated.
Non-Monotonic Reasoning’s Benefits
- We can employ non-monotonic reasoning in real-world systems like Robot navigation.
- We can choose probabilistic facts or make assumptions in non-monotonic reasoning.
Non-Monotonic Reasoning’s Drawbacks
- The old facts may be refuted by introducing new statements in non-monotonic reasoning.
- It can’t be used to prove theorems.
Reasoning’s Part In Knowledge-Based Systems
Knowledge-based systems are a type of Artificial Intelligence that uses knowledge from human specialists to make decisions and solve problems. The knowledge base and inference engine enable this. Furthermore, it employs a variety of reasoning strategies to deal with data and information ambiguities in the knowledge base.
Knowledge-based systems handle problems using deductive reasoning and non-monotonic reasoning, among other types of reasoning. It also aids in the deployment of knowledge-based systems and AI in machines, allowing them to carry out activities that require human-level intelligence and mental processes.
Conclusion
Reasoning, coupled with inference, acquired knowledge, data, and other factors, plays a critical part in creating AI machines and systems capable of completing desired tasks such as solving issues, determining the best-suited path, and so on. It gives machines human-like thinking abilities so they can assess and analyze situations and make the best decision possible. In short, it is one of the primary players that contributes to AI’s importance.
