There are many complex tasks in the field of AI that must be evaluated, whether in the field of machine learning or deep learning. In such a system, it is absolutely necessary to digitize a knowledge processing system. Knowledge representation is one such methodology that is dependent on the logical situation and allows a strategy to make a decision in the acquisition of knowledge. Humans acquire many distinct kinds of knowledge in their daily lives, but machines struggle to interpret all of them. Knowledge representation is used in such cases.
AI agents in knowledge representation algorithms tend to think and contribute to decision-making. They are capable of solving complex problems in real-world scenarios that are difficult and time-consuming for humans to interpret with the help of such complex thinking
Knowledge Representation Types
It is the knowledge segment that stores factual information in a memory and appears to be static in nature. These can be things, events, or processes, with the domain of such knowledge determining the relationship between events or things.
This knowledge is less general than declarative knowledge and is also referred to as imperative knowledge. It has the potential to disclose the completion of a specific task. It is commonly used by modern mobile robots where they can be programmed to conquer a building or navigate within a room.
Meta knowledge is a term used in the field of artificial intelligence to describe the knowledge of pre-defined knowledge. Some examples of meta knowledge include planning, tagging, and learning. This model evolves over time and employs a different specification. Precision, relevance, evaluation, predictability, comprehensiveness, disambiguation, rationalization, life expectancy, purpose, source, and reliability are all examples of meta-knowledge that a knowledge engineer may use.
This knowledge is also known as shallow knowledge, and it operates on the rule of thumb. It is very productive in the reasoning process because it solves problems based on records of previous problems or problems compiled by experts. It offers insights based on the problems it has solved in the past.
This is the most fundamental knowledge that is used and applied in problem solving. It seeks to establish a link between concepts and objects.
Let’s see the requirements for a representation of knowledge we have understood till now.
It should be adequate or complete enough to represent all types of knowledge found in the domain. It is also referred to as representational adequacy.
It should be able to manipulate the truly representative structure in order to obtain new structures that correspond to new knowledge extracted from old. It's also known as inferential adequacy.
It should be able to incorporate new information into the knowledge structure, which can then be used to direct inference processes in the best possible way. It is also referred to as inferential efficiency.
Rather than solely relying on sources, it should gain more understanding through the use of automated methods. This is referred to as acquisition.