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State space search is a process used in the field of computer science, including artificial intelligence (AI), in which successive configurations or ''states'' of an instance are considered, with the goal of finding a ''goal state'' with a desired property. Problems are often modelled as a state space, a set of ''states'' that a problem can be in. The set of states forms a graph where two states are connected if there is an ''operation'' that can be performed to transform the first state into the second. State space search often differs from traditional computer science search methods because the state space is ''implicit'': the typical state space graph is much too large to generate and store in memory. Instead, nodes are generated as they are explored, and typically discarded thereafter. A solution to a combinatorial search instance may consist of the goal state itself, or of a path from some ''initial state'' to the goal state. == Representation == In state space search a state space is formally represented as a tuple , in which: * is the set of all possible states; * is the set of possible action, not related to a particular state but regarding all the state space; * is the function that establish which action is possible to perform in a certain state; * is the function that return the state reached performing action in state * is the cost of performing an action in state . In many state spaces is a constant, but this is not true in general. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「state space search」の詳細全文を読む スポンサード リンク
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