KR and reasoning are used in AI to gain knowledge in the smartest way. It focuses on the behavior of an AI agent and ensures that it behaves more or less like a human. It is used to formalize knowledge in the knowledge base. The KR system should be able to develop new structures or ideas that it can derive from original or existing structures. An important compromise in the conception of a formalism of knowledge representation is that between expressiveness and practicality. The ultimate formalism of knowledge representation in terms of expressiveness and compactness is first-order logic (FOL). There is no formalism more powerful than that used by mathematicians to define general theorems about the world. However, FOL has two drawbacks as a knowledge representation formalism: ease of use and practicality of implementation. Top-notch logic can be intimidating, even for many software developers. Languages that don`t have all the formal power of FOL can still offer almost the same expressiveness with a user interface that`s more convenient for the average developer to understand. The problem with the practicality of implementation is that FOL is too expressive in some ways.

With FOL, it is possible to create instructions (e.g., quantization over infinite sets) that would result in a system never ending when it tries to verify them. People are good at understanding, arguing and interpreting knowledge. And with this knowledge, they are able to perform various actions in the real world. But how do machines work the same way? In this article, we will learn more about knowledge representation in AI and how it helps machines argue and interpret with artificial intelligence in the following order: Early work on the representation of computational knowledge focused on general problem solvers such as the General Problem Solver (GPS) system, developed by Allen Newell and Herbert A. Simon in 1959. These systems had data structures for planning and disassembly. The system would start with a goal. It would then break this goal down into sub-goals and develop strategies that could achieve each sub-goal. Knowledge representation and reasoning (KRR) is real-world information that a computer can understand and then use to solve complex real-world problems such as communicating with people in natural language. Knowledge representation in AI is not just about storing data in a database, but it allows a machine to learn from that knowledge and behave intelligently like a human. Different approaches are used by the knowledge representation system.

In the field of AI, knowledge of predefined knowledge is called meta-knowledge. A study of planning, marking and learning are some of the examples of metaknowledge. This model tends to change over time and use a different specification. A knowledge engineer can use different forms of meta-knowledge, which are listed below: Events: Events are the appearance of things in the real world. Everything that happens in real time is considered an event. This is an important element because it is the first thing that must be taken into account in the representation of knowledge. Expert systems gave us the terminology still used today, in which AI systems are divided into a knowledge base with facts about the world and rules and an inference engine that applies the rules to the knowledge base to answer questions and solve problems. In these early systems, the knowledge base tended to be a fairly flat structure, essentially statements about the values of the variables used by the rules. [2] One of the most active areas of knowledge representation research is currently projects related to the Semantic Web. The Semantic Web attempts to add a layer of semantics (meaning) to today`s Internet. Instead of indexing websites and pages via keywords, the Semantic Web creates large ontologies of concepts. Searching for a concept is more efficient than traditional textual searches.

Framework languages and automatic classification play a major role in the vision of the future Semantic Web. Automatic classification gives developers the technology to create order in an ever-changing network of knowledge. The definition of static ontologies that cannot evolve during operation would be very restrictive for Internet-based systems. Classification technology provides the ability to manage the dynamic environment of the Internet. KR systems must be easily accessible. Each domain or system must have the ability to identify events and decipher how different components respond. Used correctly, knowledge representation allows artificial intelligence systems to work with near-human intelligence and even perform tasks that require a large amount of knowledge. The increasing use of natural language also makes them human in their reactions. Making the right choice in the type of knowledge representation you need to integrate is crucial and allows you to get the most out of your AI system.

If you need help, we`re here. Do not hesitate to contact us. These are therefore the different components of the cycle of knowledge representation in AI. Let us now understand the relationship between knowledge and intelligence. These are the important types of knowledge representation in AI. Now let`s take a look at the knowledge representation cycle and how it works. The starting point for knowledge representation is the knowledge representation hypothesis, first developed in 1985 by Brian C. Smith has been formalized:[7] In a major 1993 paper on the subject, Randall Davis of MIT described five different roles for analyzing a knowledge representation framework: [12] The KR system should be able to acquire new knowledge through automated methods, rather than relying on human intervention. However, it should also allow the injection of information by a knowledge engineer. Let`s describe a relationship of knowledge with an organizational chart. Knowledge and logical thinking play a major role in artificial intelligence. However, you often need more than general and powerful methods to ensure intelligent behavior.

Formal logic is the most useful tool in this area. It is a language with a clear presentation, guided by certain concrete rules. The representation of knowledge depends not so much on the logic used, but on the method of logic used to understand or decipher knowledge. The diagram above shows the interaction of an AI system with the real world and the components involved in representing intelligence. Knowledge representation and reasoning (KRR, KR&R, KRĀ²) is the field of artificial intelligence (AI) dedicated to presenting information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or engaging in dialogue in natural language. Knowledge representation includes ideas from psychology[1] about how people solve problems and represent knowledge to design formalisms that facilitate the design and construction of complex systems. The representation and reasoning of knowledge also involves knowledge of logic to automate different types of thinking, such as the application of rules or the relationships of sets and subsets. Although it is not possible to find the perfect KR system at the moment, an effective system should have these characteristics: Implicit knowledge is the knowledge that exists in a human being.

It corresponds to a type of informal or implicit knowledge. It is quite difficult to articulate formally and is also difficult to communicate and share. As we can see, declarative knowledge is represented as the description of one procedural knowledge and procedural knowledge as the execution of one. Another conclusion is that declarative knowledge is called explicit, while procedural knowledge is called implicit. If knowledge can be articulated, it is declarative knowledge, and if it cannot be articulated, it is called procedural knowledge. In these early days of AI, general search algorithms such as A* were also developed. However, amorphous problem definitions for systems such as GPS meant that they only worked for very limited toy domains (e.g., the “block world”). To solve problems other than toys, AI researchers like Ed Feigenbaum and Frederick Hayes-Roth realized that it was necessary to focus systems on more limited problems.