ABSTRACT
In artificial intelligence, knowledge representation refers to the way that knowledge is encoded and stored in a computer system. This includes both the data itself and the structure and organization of the data, as well as the logical relationships between different pieces of knowledge. The goal of knowledge representation is to enable a computer system to reason about and use the knowledge it has acquired in order to solve problems, make decisions, and perform other tasks. There are many different approaches to knowledge representation, including symbolic representations, which use logical symbols and rules to encode knowledge, and subsymbolic representations, which use continuous numerical values to represent concepts. Other approaches include semantic networks, frames, and production systems. The choice of knowledge representation approach depends on the needs and goals of the application and the characteristics of the knowledge being represented. This chapter will discuss the significance of knowledge representation in AI and elusive aspects of real-time usages, techniques, approaches and development.
