Knowledge state is a subject in cognitive science as well as in artificial info and knowledge modeling. In cognitive science it is concerned with how grouping store and process information. In Artificial intelligence (AI) and knowledge modeling (KM) it is a way to store knowledge so that programs can process it and use it for example to support computer-aided design or to emulate human intelligence. AI researchers have borrowed state theories from cognitive science. There are state techniques much as frames, rules and semantic networks which have originated from theories of human aggregation processing. Since knowledge is used to achieve intelligent behavior, the fundamental goal of knowledge state is to represent knowledge in a manner as to facilitate inference from knowledge. Here are some of the issues that arise in knowledge state from an AI are:
- How do people represent knowledge?
- What is the nature of knowledge and how do we represent it?
- Should a representation scheme deal with a particular domain or should it be general purpose?
- How expressive is a representation scheme or formal language?
- Should the scheme be declarative or procedural?
There has been very little top-down discussion of the knowledge representation (KR) issues and research in this area is a well aged quilt work. There are well recognized problems such as “spreading activation”, “subsumption” and “classification.” For example a tomato could be classified both as a fruit and a vegetable. In the field of artificial intelligence, problem solving can be cut down by an appropriate choice of knowledge representation. Representing knowledge in some ways makes certain problems easier to solve. For example, it is easier to divide numbers represented in Hindu-Arabic numerals than numbers represented as Roman numerals.