Knowledge Representation in AI

Introduction

Knowledge representation is a crucial element of Artificial Intelligence. It is believed that an intelligent system needs to have an explicit representation of its knowledge to reason and make decisions.

Knowledge representation provides a framework for representing, organizing, and manipulating knowledge that can be used to solve complex problems, make decisions, and learn from data.

For example, when you see a hot tea cup, a signal immediately comes from your brain cautioning you against picking it up. If we were to make AI more sophisticated(or humanist), we would be required to feed them with more and often complex information about our world to perform the complex task, which leads to the concept of Knowledge Representation in Artificial Intelligence.

What is Knowledge Representation in AI?

Knowledge representation is a fundamental concept in artificial intelligence (AI) that involves creating models and structures to represent information and knowledge in a way that intelligent systems can use. The goal of knowledge representation is to enable machines to reason about the world like humans, by capturing and encoding knowledge in a format that can be easily processed and utilized by AI systems.

There are various approaches to knowledge representation in AI, including:

• Logical representation: This involves representing knowledge in a symbolic logic or rule-based system, which uses formal languages to express and infer new knowledge.

• Semantic networks: This involves representing knowledge through nodes and links, where nodes represent concepts or objects, and links represent their relationships.

• Frames: This approach involves representing knowledge in the form of structures called frames, which capture the properties and attributes of objects or concepts and the relationships between them.

• Ontologies: This involves representing knowledge in the form of a formal, explicit specification of the concepts, properties, and relationships between them within a particular domain.

• Neural networks: This involves representing knowledge in the form of patterns or connections between nodes in a network, which can be used to learn and infer new knowledge from data.

The Different Kinds of Knowledge: What to Represent

• Object: The AI needs to know all the facts about the objects in our world domain. E.g., A keyboard has keys, a guitar has strings, etc.
• Events: The actions which occur in our world are called events.
• Performance: It describes a behavior involving knowledge about how to do things.
• Meta-knowledge: The knowledge about what we know is called meta-knowledge.
• Facts: The things in the real world that are known and proven true.
• Knowledge Base: A knowledge base in artificial intelligence aims to capture human expert knowledge to support decision-making, problem-solving, and more.

Types of Knowledge in AI

In AI, various types of knowledge` are used for different purposes. Here are some of the main types of knowledge in AI:

• Declarative Knowledge: This knowledge can be expressed in a declarative form, such as facts, rules, or propositions. It is also called descriptive knowledge and is expressed in declarative sentences. It is often represented using logic-based representations such as knowledge graphs or ontologies. Example: The capital of France is Paris. This statement represents declarative knowledge because it is a fact that can be explicitly stated and written down. It is not based on personal experience or practical skills, but rather on an established piece of information that can be easily communicated to others.

• Procedural Knowledge: This knowledge is used to perform specific tasks or actions and is often represented using algorithms or programming languages. It is responsible for knowing how to do something. It includes rules, strategies, procedures, agendas, etc. Example: How to change a flat tire on a car, including the steps of loosening the lug nuts, jacking up the car, removing the tire, and replacing it with a spare. This is a practical skill that involves specific techniques and steps that must be followed to successfully change a tire.

• Meta-knowledge: This is knowledge about knowledge and is often used to reason about and improve the performance of AI systems. Example: To remember new information, it is helpful to use strategies such as repetition, visualization, and elaboration. This statement represents metaknowledge because it is knowledge about how to learn and remember new information, rather than knowledge about a specific fact or concept. It acknowledges that some specific techniques and strategies can be used to enhance memory and learning, and encourages the use of these techniques to improve learning outcomes.

• Heuristic Knowledge: Heuristics are based on past experiences or domain knowledge and are often used in decision-making processes to guide an AI system toward a solution. Heuristic knowledge is a type of knowledge in AI that refers to rules of thumb or strategies that are used to solve problems quickly and efficiently, but only sometimes optimally. Heuristics are often used when there is too much complexity or uncertainty in a problem to use an exact algorithm or solution. Example: When packing for a trip, it is helpful to make a list of essential items, pack versatile clothing items that can be mixed and matched, and leave room in the suitcase for any souvenirs or purchases. This statement represents heuristic knowledge because it is a practical set of rules of thumb that can be used to guide decision-making in a specific situation (packing for a trip).

• Structural Knowledge: This is knowledge about the structure of a problem or system and is often used to help AI systems decompose complex problems into simpler sub-problems that can be solved more easily. It is the basic knowledge of problem-solving. It also describes relationships between concepts such as kind of, part of, and grouping of something. Example: In the field of biology, living organisms can be classified into different taxonomic groups based on shared characteristics. These taxonomic groups include domains, kingdoms, phyla, classes, orders, families, genera, and species. This statement represents structural knowledge because it describes the hierarchical structure of the taxonomic classification system used in biology. It acknowledges that there are specific levels of organization within this system and that each level has its unique characteristics and relationships to other levels.

The Relation Between Knowledge and Intelligence

Knowledge and intelligence are related but distinct concepts. Knowledge refers to the information, skills, and understanding that an individual has acquired through learning and experience. In contrast, intelligence refers to the ability to think abstractly, reason, learn quickly, solve problems, and adapt to new situations.

In the context of AI, knowledge, and intelligence are also distinct but interrelated concepts. AI systems can be designed to acquire knowledge through machine learning or expert systems. Still, the ability to reason, learn, and adapt to new situations requires a more general intelligence that is beyond most AI systems' capabilities.

An agent can only act accurately on some input when it has some knowledge or experience about that input.

Nonetheless, using knowledge-based systems and other AI techniques can help enhance the intelligence of machines and enable them to perform a wide range of tasks.

AI Knowledge Cycle

The AI knowledge cycle is a process that involves the acquisition, representation, and utilization of knowledge by AI systems. It consists of several stages, including:

• Data collection: This stage involves gathering relevant data from various sources such as sensors, databases, or the internet.

• Data preprocessing: The collected data is then cleaned, filtered, and transformed into a suitable format for analysis.

• Knowledge representation: This stage involves encoding the data into a format that an AI system can use. This can include symbolic representations, such as knowledge graphs or ontologies, or numerical representations, such as feature vectors.

• Knowledge inference: Once the data has been represented, an AI system can use this knowledge to make predictions or decisions. This involves applying machine learning algorithms or other inference techniques to the data.

• Knowledge evaluation: This stage involves evaluating the accuracy and effectiveness of the knowledge that has been inferred. This can involve testing the AI system on known examples or other evaluation metrics.

• Knowledge refinement: Based on the evaluation results, the knowledge representation and inference algorithms can be refined or updated to improve the accuracy and effectiveness of the AI system.

• Knowledge utilization: Finally, the knowledge acquired and inferred can be used to perform various tasks, such as natural language processing, image recognition, or decision-making.

The AI knowledge cycle is a continuous process, as new data is constantly being generated, and the AI system can learn and adapt based on this new information. By following this cycle, AI systems can continuously improve their performance and perform a wide range of tasks more effectively.

Approaches to Knowledge Representation

Simple Relational Knowledge

• This type of knowledge uses relational methods to store facts.
• It is one of the simplest types of knowledge representation.
• The facts are systematically set out in terms of rows and columns.
• This type of knowledge representation is used in database systems where the relationship between different entities is represented.
• There is a low opportunity for inference.

Inheritable Knowledge

• Inheritable knowledge in AI refers to knowledge acquired by an AI system through learning and can be transferred or inherited by other AI systems.
• This knowledge can include models, rules, or other forms of knowledge that an AI system learns through training or experience.
• In this approach, all data must be stored in a hierarchy of classes.
• Boxed nodes are used to represent objects and their values.
• We use Arrows that point from objects to their values.
• Rather than starting from scratch, an AI system can inherit knowledge from other systems, allowing it to learn faster and avoid repeating mistakes that have already been made. Inheritable knowledge also allows for knowledge transfer across domains, allowing an AI system to apply knowledge learned in one domain to another.

Inferential Knowledge

• Inferential knowledge refers to the ability to draw logical conclusions or make predictions based on available data or information
• In artificial intelligence, inferential knowledge is often used in machine learning algorithms, where models are trained on large amounts of data and then used to make predictions or decisions about new data.
• For example, in image recognition, a machine learning model can be trained on a large dataset of labeled images and then used to predict the contents of new images that it has never seen before. The model can draw inferences based on the patterns it has learned from the training data.
• It represents knowledge in the form of formal logic.

Example: Statement 1: Alex is a footballer. Statement 2: All footballers are athletes. Then it can be represented as; Footballer(Alex) ∀x = Footballer (x) ———-> Athelete (x)s

Procedural Knowledge:

• In artificial intelligence, procedural knowledge refers to the knowledge or instructions required to perform a specific task or solve a problem.
• This knowledge is often represented in algorithms or rules dictating how a machine processes data or performs tasks.
• For example, in natural language processing, procedural knowledge might involve the steps required to analyze and understand the meaning of a sentence. This could include tasks such as identifying the parts of speech in the sentence, identifying relationships between different words, and determining the overall structure and meaning of the sentence.
• One of the most important rules used is the If-then rule.
• This knowledge allows us to use various coding languages such as LISP and Prolog.
• Procedural knowledge is an important aspect of artificial intelligence, as it allows machines to perform complex tasks and make decisions based on specific instructions.

Requirements For Knowledge Representation System

Representational Accuracy

Representational accuracy refers to the degree to which a knowledge representation system accurately captures and reflects the real-world concepts, relationships, and constraints it intends to represent. In artificial intelligence, representational accuracy is important because it directly affects the ability of a system to reason and make decisions based on the knowledge stored within it.

A knowledge representation system that accurately reflects the real-world concepts and relationships that it is intended to represent is more likely to produce accurate results and make correct predictions. Conversely, a system that inaccurately represents these concepts and relationships is more likely to produce errors and incorrect predictions.

Inferential Adequacy:

Inferential adequacy refers to the ability of a knowledge representation system or artificial intelligence model to make accurate inferences and predictions based on the knowledge that is represented within it. In other words, an inferentially adequate system can reason and draw logical conclusions based on its available information.

Achieving inferential adequacy requires a knowledge representation system or AI model to be designed with a well-defined reasoning mechanism that can use the knowledge stored within it. In addition, this mechanism should be able to apply rules and principles to the available data to make accurate inferences and predictions.

Inferential Efficiency

Inferential efficiency in artificial intelligence refers to the ability of a knowledge representation system or AI model to perform reasoning and inference operations in a timely and efficient manner. In other words, an inferentially efficient system should be able to make accurate predictions and draw logical conclusions quickly and with minimal computational resources.

Achieving inferential efficiency requires several factors, including the complexity of the reasoning mechanism, the amount and structure of the data that needs to be processed, and the computational resources available to the system. As a result, AI researchers and developers often employ various techniques and strategies to improve inferential efficiency, including optimizing the algorithms used for inference, improving the data processing pipeline, and utilizing specialized hardware or software architectures designed for efficient inferencing.

Acquisitional efficiency

Acquisitional efficiency in artificial intelligence refers to the ability of a knowledge representation system or AI model to effectively and efficiently acquire new knowledge or information. In other words, an acquisitionally efficient system should be able to rapidly and accurately learn from new data or experience.

Achieving acquisitional efficiency requires several factors, including the ability to recognize patterns and relationships in the data, the ability to generalize from examples to new situations, and the ability to adapt to changing circumstances or contexts. AI researchers and developers often employ various techniques and strategies to improve acquisitional efficiency, including active learning, transfer learning, and reinforcement learning.