For artificial intelligence systems to adapt to new situations, the first task is to develop a technique for machines to resolve problems 'on their own'. To do this, one needs to develop a generic method to resolve generic problems (i.e., without humans specifying the desired algorithms), and the first step on this route is to develop a general framework for describing problems that can be applied to a wide variety of contexts.
Such a system will represent many alternate situations, called states. The problem is, in essence, how to move from the start state to the goal state. To move from any one state to another is termed an operation, but from any given state, only certain other states are valid according to various preconditions. The control system decides which is the next state to move on to.
Such a system can be described through a state graph or a search tree. Explain and elaborate on the difference between a state graph and a search tree.
A state graph shows all the states, productions, and preconditions of a system whereas a search tree shows a record of state transitions explored while searching for a goal state.
Generally a state graph shows the possible states of a system. A state is a node in which a system can be at any given time. The initial state is called the start state while the desired final state is called the goal state. Depending upon the specified conditions a system can move from one state to another. A current state may lead to a single state of it may lead to multiple states but at any given point a system can reside in only one state.
Kindly refer to the attached file for example diagrams and further explanation.