Reasoning and Learning

- Overview

Artificial intelligence (AI) has come a long way since its inception, evolving from rule-based systems to complex machine learning models. One of the key milestones in this journey is the emergence of AI reasoning.

In AI, "reasoning" refers to the ability of a system to draw logical conclusions and make inferences based on existing knowledge and data, essentially mimicking human-like thought processes, while "learning" is the process of acquiring new knowledge and improving performance over time by analyzing data and adapting to new situations; both are crucial components for creating truly intelligent AI systems that can solve complex problems and make informed decisions.

How reasoning and learning work together in AI: 

• Using learned information for reasoning: AI systems can leverage the knowledge acquired through learning to make more informed decisions and draw more accurate conclusions when reasoning.
• Continual learning and adaptation: By incorporating new data and experiences through learning, AI systems can refine their reasoning capabilities and improve their performance over time.

Examples of AI applications that heavily rely on both reasoning and learning:

• Expert systems: Utilize knowledge bases and reasoning mechanisms to provide expert advice in specific domains.
• Natural Language Processing (NLP): Understanding and generating human language requires both reasoning about syntax and semantics, as well as learning from large datasets of text.
• Robotics: Robots need to reason about their environment and plan actions while continuously learning from sensor data to navigate and interact with the world.

- Concept of Reasoning in AI

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 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 (KRR) represent the information about the world in a form that can be utilized by a system to solve complex tasks.

- Characteristics of Reasoning in AI

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

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.

• Logical Inference: Using rules and logic to derive new information from existing facts, including deductive reasoning (drawing certain conclusions from known premises) and inductive reasoning (making generalizations based on patterns observed in data).
• Knowledge Representation:
• Structuring and storing information in a way that allows the AI system to easily access and manipulate it for reasoning purposes.
• Causal Reasoning: Understanding cause-and-effect relationships between events to predict outcomes and make informed decisions.
• Planning and Decision Making: Using reasoning to develop strategies and choose the best course of action based on available information and potential consequences.

- 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.  

Key aspects of learning in AI:

• Supervised Learning: Training a model with labeled data where the desired output is known, allowing the system to learn patterns and make predictions based on new input data.
• Unsupervised Learning: Identifying patterns in unlabeled data without explicit guidance, often used for data clustering and feature extraction.
• Reinforcement Learning: Learning through trial and error, where the system receives feedback signals (rewards or penalties) to adjust its behavior and optimize its actions over time.

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.