Types of Reasoning

- Overview 

Reasoning is the process of using existing knowledge to draw conclusions, make predictions, or construct expectations. In the development of artificial intelligence, reasoning ability is crucial. Reasoning, therefore, is the use of prior knowledge to make inferences, form hypotheses, or develop strategies to solve problems. 

In order to understand the human brain, the way the brain thinks, and the way the brain reaches conclusions about certain things, we need the help of reasoning, which is why it is so important in AI.  

- Two Main Forms of Reasoning in AI

Reasoning is primarily categorized into two main forms: formal reasoning which relies on strict logical rules and symbolic representation, and natural language reasoning which deals with understanding and reasoning within the complexities of everyday language, more closely mirroring human thought processes.

• Formal reasoning: Uses precise logical systems with clearly defined rules and symbols.Often used in mathematics and computer science for theorem proving and program verification. Examples: "All cats are mammals; therefore, my cat is a mammal."
• Natural language reasoning: Involves interpreting and reasoning with language as it is naturally spoken or written. Can handle ambiguity and context, which is common in everyday conversation. Examples: "The weather is bad today, so we should probably stay inside."

- Major Categories of Reasoning in AI

• Deductive reasoning: Involves drawing conclusions from known data that are logically connected. In AI, deductive reasoning is a type of propositional logic that requires many rules and facts.
• Abductive reasoning: Similar to inductive reasoning, but allows for making estimates to reach simple conclusions. Abductive reasoning can be helpful for troubleshooting and decision-making, especially when there are uncertainties.
• Analogical reasoning: Involves finding similarities between two or more things and using those characteristics to find other qualities in common.
• Probabilistic reasoning: Involves using probability theory to explain how beliefs should change when information is incomplete or uncertain.
• Statistical reasoning: Involves combining historical insights with modern applications to enhance learning capabilities. 

[More to come ...]