what is state space search in ai

State space search is a process used in computer science, including artificial intelligence (AI), to find a solution to a problem by exploring all possible states of the problem. It is a problem-solving technique that searches through all possible states of a problem to find a solution. A state space is a mathematical representation of a problem that defines all possible states that the problem can be in. In search algorithms, we use a state space to represent the current state of the problem, the initial state, and the goal state. State space search algorithms can be classified as uninformed or informed. Uninformed search methods do not have any prior information about the goals location, while informed search methods use additional information to guide the search towards the goal. State space search often differs from traditional computer science search methods because the state space is implicit, meaning that 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. State space search algorithms have a wide range of applications in various fields, including artificial intelligence, robotics, game playing, computer networks, operations research, bioinformatics, cryptography, and supply chain management.