Control Strategy in Artificial Intelligence scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space. It helps us to decide which rule has to apply next without getting stuck at any point. These rules decide the way we approach the problem and how quickly it is solved and even whether a problem is finally solved.
Control Strategy helps to find the solution when there is more than one rule or fewer rules for finding the solution at each point in problem space. A good Control strategy has two main characteristics:
Control Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Control strategy should be Systematic
Though the strategy applied should create the motion but if do not follow some systematic strategy than we are likely to reach the same state number of times before reaching the solution which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion (over the course of several steps) as well as for local motion (over the course of single step).
Breadth-First Search: It searches along the breadth and follows first-in-first-out queue data structure approach. It will start to scan node A first and then B-C-D-E-F.
Depth-First Search: It searches along the depth and follows the stack approach. The sequence for scanning nodes will be A-B-D-E-C-F, it scans all the sub-nodes of parent nodes and then moves to another node.
Widely used Control Strategies are Breadth-First Search, Depth-First Search, Generate and Test, Hill-Climbing, Best-first search, Problem Reduction and many more.