Uninformed Search Strategies:

To be able to undertake a regular search, our entire agent ought to know is the starting state, the possible operators and how to check whether the goal has been reached. Once these have been described, we might then select a search method for the agent: a pre-defined way in which the operators will be applied.

The example we will use is the case of a genetics professor finding  for a name for her newborn  boy - of course, it might  only contain the letters D, A and N. The states in this search are strings of letters (but only Ds, As and Ns), and the starting  state is an empty string. Also, the operators available are: (i) add an 'N' to an existing string (ii) add a 'D' to an existing string and (iii) add an 'A' to an existing string. The goal check is feasible using a book of boy's names against which the professor may check a string of letters.

To help us think about the different kind of search method  , we use 2 analogies. Initially, we imagine that the professor keeps an agenda of actions to undertake, like wise : add an  'A'  to  the string  'AN'.  So, the agenda contain of pairs (O,S)  of states and operators, whereby the operator is to be applied to the state. At the top of the plan, the action is the one which is carried out, then that action is removed. How these actions are added to the plan differs for each search method. Next, we think of a search graphically: by making each state a node in a graph and every operator an edge, we might think of the search progressing as movement from node to node along edges in the graph. We then allow ourselves to talk about nodes in a search space (rather than the graph) and we say that a node in a search space has been expanded if the state that node represents has been visited and searched from. Notice that graphs which have no cycles in them are called trees and many Artificial Intelligence searches can be represented as trees.