Anti-computer tactics
Human methods against game-playing computers / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Anti-computer tactics?
Summarize this article for a 10 year old
Anti-computer tactics are methods used by humans to try to beat computer opponents at various games, most typically board games such as chess and Arimaa. They are most associated with competitions against computer AIs that are playing to their utmost to win, rather than AIs merely programmed to be an interesting challenge that can be given intentional weaknesses and quirks by the programmer (as in many video game AIs). Such tactics are most associated with the era when AIs searched a game tree with an evaluation function looking for promising moves, often with Alpha–beta pruning or other minimax algorithms used to narrow the search. Against such algorithms, a common tactic is to play conservatively aiming for a long-term advantage. The theory is that this advantage will manifest slowly enough that the computer is unable to notice in its search, and the computer won't play around the threat correctly. This may result in, for example, a subtle advantage that eventually turns into a winning chess endgame with a passed pawn. (Conversely, attempting to lure an AI into a short-term "trap", inviting the play of a reasonable-seeming to humans but actually disastrous move, will essentially never work against a computer in games of perfect information.)
The field is most associated with the 1990s and early 2000s, when computers were very strong at games such as chess, yet beatable. Even then, the efficacy of such tactics was questionable, with several tactics such as making unusual or suboptimal moves to quickly get the computer out of its opening book proving ineffective in human-computer tournaments. The rise of machine learning has also dented the applicability of anti-computer tactics, as machine learning algorithms tend to play the long game equally as well if not better than human players.