Editing Artificial intelligence (section)

==== State space search ====
[[State space search]] searches through a tree of possible states to try to find a goal state.<ref>[[State space search]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 3}}</ref> For example, [[Automated planning and scheduling|planning]] algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called [[means-ends analysis]].{{Sfnp|Russell|Norvig|2021|loc=sect. 11.2}}

[[Brute force search|Simple exhaustive searches]]<ref>[[Uninformed search]]es ([[breadth first search]], [[depth-first search]] and general [[state space search]]): {{Harvtxt|Russell|Norvig|2021|loc=sect. 3.4}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=113–132}}, {{Harvtxt|Luger|Stubblefield|2004|pp=79–121}}, {{Harvtxt|Nilsson|1998|loc=chpt. 8}}</ref> are rarely sufficient for most real-world problems: the [[Search algorithm|search space]] (the number of places to search) quickly grows to [[Astronomically large|astronomical numbers]]. The result is a search that is [[Computation time|too slow]] or never completes.<ref name="Intractability and efficiency and the combinatorial explosion"/> "[[Heuristics]]" or "rules of thumb" can help prioritize choices that are more likely to reach a goal.<ref>[[Heuristic]] or informed searches (e.g., greedy [[Best-first search|best first]] and [[A* search algorithm|A*]]): {{Harvtxt|Russell|Norvig|2021|loc=sect. 3.5}}, {{Harvtxt|Poole|Mackworth|Goebel|1998|pp=132–147}}, {{Harvtxt|Poole|Mackworth|2017|loc=sect. 3.6}}, {{Harvtxt|Luger|Stubblefield|2004|pp=133–150}}</ref>

[[Adversarial search]] is used for [[game AI|game-playing]] programs, such as chess or Go. It searches through a [[Game tree|tree]] of possible moves and countermoves, looking for a winning position.<ref>[[Adversarial search]]: {{Harvtxt|Russell|Norvig|2021|loc=chpt. 5}}</ref>