To recap, we now have some characterizations of "AI", that when an "AI" problem arises, you will be able to put all into context exactly, find the correct techniques and apply them. We have interacted the agents language so that we can talk about intelligent tasks and how to carry them out. We have also looked at search in the general such case that is central to AI problem solving. Most pieces of software have to deal regarding to data of some type, and in AI we use the more grandiose title of "knowledge" to stand for data including (i) facts, such as the temperature of a patient (ii)procedures, such as how to treat a patient with a high temperature and (iii) meaning, like why a patient with a high temperature should not be given a hot bath.
Accessing and utilizing all these kinds of information will be vital for an intelligent agent to act rationally. Just for this reason, knowledge representation is our final general consideration before we look at particular problem types.
To a large extent, the way in that you categorize information suitable and to generated by your intelligent agent will be dictated by the type of problem you are addressing. In fact, the best ways of representing knowledge for particular techniques are known. In fact, as with the problem of how to search, you could be use a lot of flexibility in the way you represent information. Therefore, it is worth looking at four(4) general schemes to representing knowledge, namely logic, semantic networks, introduction rules and frames. Knowledge representation continues can be a much-researched topic in "AI' is of the realization fairly early that how information is orderly can often make or break an "AI" application.