In artificial intelligence (AI), reasoning is the process by which intelligent agents use the information or data at their disposal to conclude, decide, and resolve issues. It is a fundamental feature of artificial intelligence that enables machines to mimic human thought processes.
1. First of all, what is reasoning?
- Thinking logically in order to make inferences or draw conclusions is known as reasoning.
- In AI, what are inferences?
The process of applying reasoning techniques to infer new information from preexisting facts or data is known as inference.
In other words:
Inference is the process of using logic or rules to determine something else based on what you already know.
- In AI, what are inferences?
- It means giving a machine the ability to:
- Recognize a challenge
- Apply rules or knowledge.
- Infer new facts
- What Does “Infer” Mean?
Infer means:
To figure something using using facts, reasoning, or hints — even when it wasn’t directly stated.
Simple Definition:
Infer = Make an intelligent logical guess
Examples from Real Life:
Example 1:
You see dark clouds in the sky.
You infer that It may rain soon
Example 2 (context of AI):
AI Knows: All humans are mortal.
AI Knowns: Socrates is a human.
AI infers: Socrates is mortal.
Infer vs. See
See = You receive information directly
“The sky is blue.” (You just watch it.)
Infer = You determine it from hints
You infer it’s raining because you see people carrying umbrellas. Similar to how humans think and solve problems, artificial intelligence (AI) uses logic, rules, or patterns to infer new facts.
- What Does “Infer” Mean?
- Make decisions or solve issues.
Example: A robot can reason that “Socrates is mortal” if it is aware that “All humans are mortal” and “Socrates is a human.”
2. Why is Reasoning Important in AI?
Reasoning helps AI agents:
- Make intelligent decisions
- Interpret ambiguous information
- What Does “Interpret Ambiguous Information” Mean?
Information that is ambiguous means:
Uncertainty, ambiguity, or information with multiple meanings.
Interpret ambiguous information means:
The AI attempts to comprehend or make sense of such ambiguous data by selecting the most likely meaning based on rules, reasoning, or context.
Easy Way to Explain:
Let’s say someone says:
“I saw her duck.”
This sentence is ambiguous because:
Is a “duck” a bird?
Or does it mean she bent down quickly?
A human (or AI) must consider context in order to interpret this.
- What Does “Interpret Ambiguous Information” Mean?
- Predict future states
- What Does “Predict Future States” Actually Mean?
To Predict future states means:
The AI makes predictions about the future based on reasoning and current information.
AI makes predictions based on data, logic, or patterns, just like humans do (“If I touch fire, it will burn”).
In simple words,
AI predicts and estimates what the next circumstance or state will be.
- What Does “Predict Future States” Actually Mean?
- Explain outcomes or actions
- Learn from observations
- Learn from Observations”: What Does It Mean?
To Learn from observations means:
Without being explicitly programmed for every scenario, an AI system observes, records, or receives data and then automatically determines patterns or rules.
In simple words:
AI observes examples, detects patterns, and gains new knowledge.
- Learn from Observations”: What Does It Mean?
It bridges the gap between raw data and meaningful action.
3. Types of Reasoning in AI
AI uses a variety of reasoning techniques, including:
1. Deductive Reasoning (Top Down Logic)
- Draws particular conclusions from general facts.
- based on rules and formal logic.
Example:
First Premise: All cats are animals.
Second Premise: Luna is a cat.
Conclusion: Luna is an animal.
In logic and reasoning, a premise is a statement or assumption that is assumed to be true and forms the basis of an argument or conclusion.
In your example:
- The first premise is that “all cats are animals.” This is a generalization, a rule or truth that applies to everyone.
- The second premise is that “Luna is a cat.” This is something that is true about Luna.
We put these two ideas together using deductive reasoning to come to a logical conclusion:
- Conclusion: “Luna is an animal.”
✅ Used in expert systems, logic programming, rule-based systems.
2. Inductive Reasoning or Bottom Up Logic
- Starts from specific examples and infers general rules.
- Common in machine learning.
Example:
Observation: The sun rose today.
Observation: The sun rose yesterday.
In Conclusion: The sun always rises in the morning. (Generalization)May lead to incorrect generalizations.
3. Abductive Reasoning (Best Explanation)
- Infers the most likely cause from observations.
- Common in diagnosis systems.
Example:
Observation: The grass is wet.
Possible cause: It rained last night.
Conclusion: It probably rained last night.🩺 Used in medical diagnosis, fault detection.
4. Analogical Reasoning
- Solves new problems based on similarities to known problems.
Example:
- If solving a maze works by following the left wall, try the same for a new maze.
A maze is a complicated or puzzling system of routes.
In this puzzle, you have to figure out how to move from one place to another, often through twisting paths and dead ends.
In context of Analogical Reasoning:
This is an example of learning from experience or using a past solution for a new but similar problem.
This is an example of applying knowledge gained from experience or a previous solution to a new but related problem
- Consider the time you used to solve a physical maze by consistently placing your hand on the left wall, as in a puzzle book or video game.
- Since it worked in the past, you apply the same strategy (left wall) when you encounter another maze.
You’re not proving logically that it will work — you’re guessing based on similarity.
Key Point:
- Maze here = A symbolic or literal puzzle/problem.
- Analogical reasoning = “It’s worked before, so it might work here too.”
Used in case-based reasoning systems.
What is Case-Based Reasoning (CBR)?
Case-Based Reasoning is a method in Artificial Intelligence where past cases (examples/problems) are stored and used to solve new problems by finding similar cases.
“If a solution worked for a similar problem before, try it again (with or without changes).”