To recap, now we have some characterizations of Artificial Intelligence, so when an Artificial Intelligence problem arises, you will enable to put it into context, search the right techniques and apply them. We have introduced the agent's language so that we may talk about intelligent job and how to carry them out. We have also looked at search in the common case, which is central to AI problem solving. Most pieces of software have to deal with data of some type, and in AI we use the additional grandiose title of "knowledge" to stand for data including
(i) procedures, like how to treat a patient with a high temperature
(ii) facts, like the temperature of a patient and
(iii) meaning, likewise why a patient with a high temperature should not be given a hot bath. Accessing and using all these kinds of information will be very important for an intelligent agent to act rationally. For this reason, knowledge representation is our final common consideration before we look at specific problem types.
To a big extent, the way in which you arrange information available to and generated by your intelligent agent will be dictated by the type of problem you are addressing. Regularly, the best ways of representing knowledge for particular techniques are known. But, as with the problem of how to search, you will have to great flexibility in the way you represent information. so, it is worth looking at 4 common schemes for representing knowledge, namely frames ,semantic networks, production rules and logic. Knowledge representation continues to be a much-researched topic in AI because of the realization early on that how information is arranged may often make or break an AI application.