In order to solve complex problems encountered in artificial intelligence, one needs both a large amount of knowledge and some mechanism for manipulating that knowledge to create solutions.
Knowledge and Representation are two distinct entities. They play central but distinguishable roles in intelligent system.
Knowledge is a description of the world. It determines a system’s competence by what it knows.
Representation is the way knowledge is encoded. It defines a system’s performance in doing something.
Different types of knowledge require different kinds of representation.
The knowledge Representation models/mechanisms are often based on:
- Logic
- Rules
- Frames
- Semantic Net
Knowledge is categorized into two major types.
A variety of ways of representing knowledge have been exploited in AI programs.
There are two different kinds of entities, we are dealing with.
- Facts: Truth in some relevant world. Things we want to represent.
- Representation of facts in some chosen formalism. Things we will actually be able to manipulate.
These entities are structured at two levels:
- The knowledge level, at which facts are described.
- The symbol level, at which representation of objects are defined in terms of symbols that can be manipulated by programs.
