Reasoning and Learning

- Overview

In AI, reasoning is essential so that machines can think rationally like a human brain and act like a human. 

Reasoning is the mental process of drawing logical conclusions and making predictions from existing knowledge, facts, and beliefs. Or we can say, "Inference is a method of inferring facts from existing data. (i.e., a conclusion reached on the basis of evidence and reasoning.)" It is a general process of thinking rationally and finding valid conclusions. 

Reasoning plays a big role in the AI process. Reasoning, therefore, can be defined as the logical process of drawing conclusions, making predictions, or constructing approaches to a particular idea with the help of existing knowledge. 

In AI, reasoning is very important, because to understand the human brain, how the brain thinks, how it draws conclusions about certain things, all these tasks need the help of reasoning.

- Reasoning

Reasoning in artificial intelligence (AI) is the process of using logical rules and principles to derive new information from existing information. AI systems use reasoning to make inferences, draw conclusions, and solve problems. 

Deductive reasoning is figuring out new information from known information that is logically tied to it. Inductive reasoning uses generalization to conclude limited data from specific facts or data to a general assertion or conclusion.

Reasoning is a key component of AI applications such as expert systems, natural language processing, and machine learning. It allows computers to draw logical conclusions from data and knowledge, and to make decisions based on those conclusions. 

Knowledge representation is another aspect of AI. It is a way to describe how knowledge can be represented in AI. It involves more than just storing data into a database. It also enables an intelligent machine to learn from that knowledge and experiences so that it can behave intelligently like a human. 

Knowledge Representation and Reasoning, KR2 or KRR, represent the information about the world in a form that can be utilized by a system to solve complex tasks.

- Learning

AI includes machine learning, deep learning, and natural language processing. These technologies allow computers to "learn" from experience and perform human-like tasks like data visualization or data manipulation.  

AI learning processes include:

• Processing input-output pairs: A collection of input-output pairs are processed for a specific function, and outputs are predicted for new inputs.
• Learning models: There are two main groups of learning models: supervised and unsupervised.
• Self-learning systems: These systems are adaptive and can acquire and renew knowledge on their own over time.
• Machine learning: A system can "learn" from data without direct instruction. Machine learning is a branch of AI that uses algorithms trained on data to produce models that can perform tasks. 

- Machine Learning Vs. Machine Reasoning

Machine learning is based on the statistical identification of hidden patterns in large amounts of data, while machine reasoning is based on using facts and drawing conclusions from those facts. That is to say, machine learning is based on the analysis of many examples (preferably classified) of the phenomenon you want to learn, and the machine independently builds a model to automatically classify new examples. 

In machine reasoning, the system receives semantic models and reasoning methods from the outside, and then the machine draws conclusions on new examples. Another difference between the two approaches is that machine learning deals with pattern recognition, while machine reasoning deals with understanding relationships and drawing conclusions from facts.

Machine reasoning uses concepts and ideas encoded as symbols and then draws logical conclusions consistent with common sense. Inference systems represent data through a semantic knowledge graph, enabling machines to understand the meaning of data through the semantics encoded in the graph, and to draw conclusions about that data by analyzing concept graphs and projecting them onto new data.