Representations and Mappings

In simple terms knowledge, representation is a technique which is used in artificial intelligence with the fundamental goal of representing knowledge in a standard manner such that it facilitates inferencing or reasoning or resolution from that knowledge. It analyzes how to think formally and how to use a symbol to represent a knowledge along with different situation that allows inferencing knowledge representation to help to address the different problems like how do we represent facts about the world, how do we reason about the facts, what kinds of representations are appropriate and how can an agent perform well in reasoning. There are the variety of ways to represent knowledge or facts which have been exploited in AI programs. In all variety of knowledge representations we deal with two kinds of entities and they are facts which are referred to the truth in some relevant world. These are the things we want to represent and the second entity will be the representations of facts which are acquired from some chosen formalism. These are the things which we will actually be able to manipulate. The above two entities can be structured at two levels : The knowledge level of which facts is described. The symbol level at which representations of objects at the knowledge level are defined in terms of symbols that can be manipulated by programs. The facts and the representations are linked with the two-way mappings. This link is called representation mappings. The forward representation mapping maps from the facts to the representations. The backward representation mapping goes the other way that is from representations to facts.