Editing Computer chess (section)

== Impact of AI on chess ==
=== Revolutionizing chess strategy ===
The introduction of artificial intelligence transformed the game of chess, particularly at the elite levels. AI greatly influenced defensive strategies. It has the capacity to compute every potential move without concern, unlike human players who are bound to emotional and psychological impacts from factors such as stress or tiredness. As a result, many positions once considered not defensible are now recognized as defensible. 

After studying millions of games, chess engines made new analysis and improved the existing theories of opening. These improvements led to the creation of new ideas and changed the way players think throughout all parts of the game.<ref name="ChessbaseAIRimpact">ChessBase. (2024). How the AI revolution impacted chess (1/2). ChessBase. [https://en.chessbase.com/post/how-the-ai-revolution-impacted-chess-1-2 ChessBase.com]. Accessed 2025 Feb 11.</ref>In classical chess, elite players commonly initiate games by making 10 to 15 opening moves that align with established analyses or leading engine recommendations.<ref>Kahn, J. (2019). Can Chess Survive Artificial Intelligence? The New Atlantis, (58), 16-35. https://www.thenewatlantis.com/publications/can-chess-survive-artificial-intelligence</ref>

=== Cheating and fair play ===
{{see also|Cheating in chess}}
Unlike traditional over-the-board tournaments where handheld metal detectors are employed in order to counter players attempts at using electronic assistance, fair-play monitoring in online chess is much more challenging.

[https://www.europechess.org/european-online-chess-championship-2020-regulations/ During the 2020 European Online Chess Championship], which saw a record participation of nearly 4000 players over 80 participants were disqualified for cheating—most from beginner and youth categories.<ref name="MumbaiMirrorCheating">Cheating and fair play. [https://mumbaimirror.indiatimes.com/sport/others/european-online-chess-championship-over-80-players-disqualified-for-violating-fair-play-rules/articleshow/75910673.cms European online chess championship: Over 80 players disqualified for violating fair play rules]. MumbaiMirror.indiatimes.com. May 29, 2020. Accessed 2025 Feb 11.</ref> The event underscored the growing need for advanced detection methods in online competitions.

In response to these issues, chess platforms such as [[Chess.com]] developed AI-based statistical models which track improbable moves by a player and compare them to moves that could be made by an engine. Expert examination is conducted for all suspected cases, and the findings are published on a regular basis. FIDE introduced AI behavior-tracking technology to strengthen [[anti-cheating|anti-cheating measures]] in online events.<ref name="Research"> Duca Iliescu DM. [https://pmc.ncbi.nlm.nih.gov/articles/PMC7759436/#ref3 The Impact of Artificial Intelligence on the Chess World.] JMIR Serious Games. 2020 Dec 10;8(4):e24049. doi: 10.2196/24049. PMID: 33300493; PMCID: PMC7759436. </ref>

==== Challenges in cheat detection ====
AI-based detection systems use a combination of machine learning to track suspicious player actions in different games. This is done by measuring discrepancies between the real moves and the predicted moves derived from the available statistics. Players of unusually high skill level or unusual strategies that can imitate moves characteristic of automated chess systems. Each case is examined by a human expert to ensure that the decision is correct before any actions are made to guarantee fairness and accuracy.<ref name="Research" />

=== Aligning AI with humans ===
The Maia Chess project was began in 2020 by the [[University of Toronto]], [[Cornell University]], and [[Microsoft Research]]. Maia Chess is a [[Neural network (machine learning)|neural network]] constructed to impersonate a human’s manner of playing chess based on skill. Each Maia models was tested on 9 sets of 500,000 positions each, covering rating levels from 1100 to 1900. They perform best when predicting moves made by players at their targeted rating level, with lower Maias accurately predicting moves from lower-rated players (around 1100) and higher Maias doing the same for higher-rated players (around 1900). The primary goal of Maia is to develop an AI chess engine that imitates human decision-making rather than focusing on optimal moves. Through personalization across different skill levels, Maia is able to simulate game styles typical for each level more accurately.<ref>@inproceedings{McIlroy_Young_2020, series={KDD ’20},
   title={Aligning Superhuman AI with Human Behavior: Chess as a Model System},
   url={https://arxiv.org/abs/2006.01855},
   DOI={10.1145/3394486.3403219},
   booktitle={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining},
   publisher={ACM},
   author={McIlroy-Young, Reid and Sen, Siddhartha and Kleinberg, Jon and Anderson, Ashton},
   year={2020},
   month=aug, pages={1677–1687},
   collection={KDD ’20} }
</ref><ref name="ScienceFocusAIChess">{{cite web 
| url = https://www.sciencefocus.com/future-technology/ai-has-dominated-chess-for-25-years-but-now-it-wants-to-lose 
| title = AI has dominated chess for 25 years, but now it wants to lose 
| website = Science Focus 
| publisher = Immediate Media Company Ltd. 
| access-date = 2025-02-11
| date = 2023-02-13}}</ref>

=== Chess and LLMs ===
While considered something done more for entertainment than for serious play, people have discovered that [[large language model]]s (LLMs) of the type created in 2018 and beyond such as [[GPT-3]] can be prompted into producing chess moves given proper language prompts.  While inefficient compared to native chess engines, the fact that LLMs can track the board state at all beyond the opening rather than simply recite chess-like phrases in a [[Hallucination (artificial intelligence)|dreamlike state]] was considered greatly surprising.  LLM play has a number of quirks compared to engine play; for example, engines don't generally "care" how a board state was arrived at.  However, LLMs seem to produce different quality moves for a chess position reached via strong play compared to the same board state produced via a set of strange preceding moves (which will generally produce weaker and more random moves).<ref>{{cite web |url=https://dynomight.net/more-chess/ |title=OK, I can partly explain the LLM chess weirdness now |date=November 21, 2024 }}</ref>