Knowledge Representation.

solving complex AI problems requires large amounts of knowledge and mechanisms for manipulating that knowledge. The inference mechanisms that operate on knowledge, relay on the ways knowledge is represented. A good knowledge representation model allows for more powerful inference mechanisms that operate on them. While representing knowledge one has to consider two things.

 

1. Facts, which are truths in some relevant world.

2. Representation of facts in some chosen formalism . These are the things

    which are actually manipulated by inference mechanism.

Knowledge representation schemes are useful only if there are functions that map facts to representations and vice versa. AI is more concerned with a natural language representation of facts and the functions which map natural language sentences into some representational formalism. An appealing way of representing facts is using the language of logic. Logical formalism provides a way of deriving new knowledge from the old through mathematical deduction. In this formalism, we can conclude that a new statement is true by proving that it follows from the statements already known to be facts.