State Space Search in Artificial Intelligence

State space search is a fundamental technique in artificial intelligence for finding solutions to problems represented as states within a defined search space. It involves systematically exploring possible states and transitions between them using search algorithms like breadth-first search, depth-first search, A* search, and more. Each state represents a configuration or snapshot of the problem, and transitions correspond to actions that transform one state into another. The goal is to find a sequence of actions that lead from an initial state to a goal state, optimizing criteria such as path length, cost, or heuristic estimates. State space search is widely applicable in problem-solving domains like planning, robotics, and game playing.