Answers.Body: <p>Not all games (or even board games) are computationally algorithmic. Even the least skilled player is likely to trounce the hottest pattern-matching algorithm in a game of <strong><a href="https://en.wikipedia.org/wiki/Pictionary" rel="noreferrer">Pictionary</a></strong> (for example).</p>
  
  <p><img src="https://i.sstatic.net/RDIuC.png" alt=""></p>
  
  <p>If you want to say that the movement of pieces upon successful completion of a task is only ancelary to the object of the game, than your answer will be largely self-selecting. A sufficiently sophisticated algorithm will brute force a computational problem better than human intuition&hellip; <a href="https://en.wikipedia.org/wiki/AI_effect" rel="noreferrer"><strong>eventually.</strong></a></p>
  
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Answers.Text: Citation needed.
Answers.UserDisplayName: Raphael
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Answers.Text: Yeah, I think Pictionary might be pretty easy to beat these days. Since it's team-based it's not clear what a human vs. computer matchup would look like, though.
Answers.UserDisplayName: Matthew Gray
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Answers.UserDisplayName: Robert Cartaino
Answers.Body: <p>Artificially intelligent computer programs should be able to be at the same level or beat humans at every game that we play.  This is because games follow rules that are scriptable, and <a href="http://www.aaai.org/ojs/index.php/aimagazine/article/view/2310" rel="nofollow">artificial intelligence</a> is designed to focus on one specific game and learn from its failures.  The difference between humans and artificial intelligence is that artificial intelligence focuses on one specific task like learning to master Go while our brain is dedicated to mastering multiple tasks like...living.  Even Arimaa, a game designed to be difficult for artificially intelligent systems was beaten by a bot called Sharp: <a href="https://en.wikipedia.org/wiki/Arimaa" rel="nofollow">https://en.wikipedia.org/wiki/Arimaa</a>.  </p>
  
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Answers.Text: Many games have social elements. How do you script those?
Answers.UserDisplayName: Raphael
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Answers.Text: This is a good point, but I am not sure I understand.  Many of the games I have played or seen artificial intelligence play have clearly defined rules.  By social, do you mean that the rules can change based on the people playing?  For example, my  cousins play the card game "War" a bit differently as they use the "Joker" card as the highest card while my brother and I don't use that card.  Can you clarify what you mean by social?
Answers.UserDisplayName: Veena Ghorakavi
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Answers.Text: In Werewolf, the whole gameplay revolves around people talking and deciding whom to take out; there are barely any rules beyond that. But even *if* you have rules, think of cooperative games like Pandemic; how does the AI *discuss*? What about games that involve moral decisions like e.g. This War of Mine? In summary, I think your claim "computer programs [...] beat humans at every game that we play" is wrong -- precisely because *not* every game follows scriptable rules.
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Answers.Body: <p>In some sense, Go returned to this category in 2023 with the <a href="https://arxiv.org/pdf/2211.00241" rel="nofollow noreferrer">discovery</a> of an inherent weakness in superhuman Go-playing AIs. There is a particular pattern that the AI consistently misinterprets, so forcing that kind of position leads the AI to play blunders that are clear even to a Go novice. Figure J.2 in the paper shows an example. The idea is to create a dead group and let the AI enclose it, but not capture it (which is perfectly correct behaviour). The AI them mistakenly considers the enclosing group unconditionally alive, even though for that it would have to actually capture the dead group. It then lets the enclosing group be captured. The loss involves a misread capture race; the particular blunder move is clear even to a Go novice.</p>
  <p>Importantly, though the strategy was discovered through training an adversarial AI, it can be played by an unaided human. Also importantly, this is not a trivially fixable bug. The main author of KataGo confirms this <a href="https://www.reddit.com/r/MachineLearning/comments/yjryrd/comment/iuqm4ye/?utm_source=reddit&amp;utm_medium=web2x&amp;context=3" rel="nofollow noreferrer">here</a>, pointing out also that KataGo, a commonly-used superhuman Go AI, sometimes misreads specific positions that arise within regular gameplay when they get out of the space explored in self-play. This means that superhuman Go AIs require various ad hoc additions to their training. The original paper discvered two exploits, one that relied on a particular ruleset and the more legitimate one described above, but it's possible that there are yet more exploits that would be found next if this got patched.</p>
  <p>Since there aren't big human-versus-AI events in Go these days, it's hard to evaluate how (un)fixable this problem is, but professional Go players take it seriously. <a href="https://www.youtube.com/watch?v=H4DvCj4ySKM" rel="nofollow noreferrer">Here</a> is a dan-level professional playing the strategy out and reflecting on what it means for the game as a piece of culture.</p>
  <p>As an aside, I find this fascinating; it's what got me into Go, through <a href="https://franklantz.substack.com/p/the-afterlife-of-go" rel="nofollow noreferrer">this essay</a> (which I also recommend, even if you don't know Go at all). I learned the game to understand what this kind of exploit means, and I think I've figured it out. It shows one component of human intelligence that we have not replicated so far in AI: humans have a kind of &quot;constant vigilance&quot;. A human encountering this strategy would think &quot;this is weird, my enemy is up to something&quot;. They would spot where the trap seemed to lie, and play the obvious safeguarding move to block it, even if they never saw anything like it before. I have no idea how humans do this. I guess it shows that intelligence is much more of an ill-defined, ad-hoc, improvisational kind of thing than generally assumed.</p>
  
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Answers.UserDisplayName: Kotlopou
Answers.Body: <p>For many years, the focus has been on games with perfect information. That is, in Chess and Go both of us are looking at the same board. In something like Poker, you have information that I don't have and I have information that you don't have, and so for either of us to make sense of each other's actions we need to model what hidden information the other player has, and <em>also</em> manage how we leak our hidden information. (A poker bot whose hand strength could be trivially determined from its bets will be easier to beat than a poker bot that doesn't.)</p>
  <p>Current research is switching to tackling games with imperfect information. Deepmind, for example, <a href="http://www.businessinsider.com/google-deepmind-could-play-starcraft-2016-3" rel="nofollow noreferrer">has said</a> they might approach Starcraft next.</p>
  <p>I don't see too much difference between video games and board games, and there are several good reasons to switch to video games for games with imperfect information.</p>
  <p>One is that if you want to beat the best human to be a major victory, there needs to be a pyramid of skill that human is atop of--it'll be harder to unseat the top Starcraft champion than the top Warcraft champion, even though the bots might be comparably difficult to code, just because humans have tried harder at Starcraft.</p>
  <p>Another is that many games with imperfect information deal with reading faces and concealing information, which an AI would have an unnatural advantage at; for multiplayer video games, players normally interact with each other through a server as an intermediary and so the competition will be more normal.</p>
  
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Body: <p>Significant AI vs human board game matches include:</p>
  <ul>
  <li><strong>chess</strong>: <a href="https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)#Deep_Blue_versus_Kasparov" rel="nofollow noreferrer">Deep Blue vs Kasparov</a> in 1996,</li>
  <li><strong>go</strong>: <a href="https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol" rel="nofollow noreferrer">DeepMind AlphaGo vs Lee Sedol</a> in 2016,</li>
  </ul>
  <p>which demonstrated that AI challenged and defeated professional players.</p>
  <p>Are there known board games left where a human can still win against an AI? I mean based on the final outcome of authoritative famous matches, where there is still the same board game where AI cannot beat a world champion of that game.</p>
  
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Comments.Text: Can't a human + computer still beat any AI system in these games?  Keeping overall computational power the same in both cases, of course.  Kasparov said he staged human vs. AI chess competitions, and amateurs + low powered computers beat experts and supercomputers.
Comments.UserDisplayName: yters
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Comments.Text: Well, games like Ludo are almost entirely based on luck. So there's a change of about 50% that a human would win from an "AI" in this case.
Comments.UserDisplayName: Kevin
Tags: <reference-request><game-theory><history><state-of-the-art><board-games>
Title: Is there any board game where a human can still beat an AI?
UserDisplayName: kenorb
ThreadText: # Is there any board game where a human can still beat an AI?
  
  (Asked by: username_0 on 2016-08-11)
  
  Significant AI vs human board game matches include:
  
  * **chess**: [Deep Blue vs Kasparov](https://en.wikipedia.org/wiki/Deep_Blue_(chess_computer)#Deep_Blue_versus_Kasparov) in 1996,
  * **go**: [DeepMind AlphaGo vs Lee Sedol](https://en.wikipedia.org/wiki/AlphaGo_versus_Lee_Sedol) in 2016,
  
  which demonstrated that AI challenged and defeated professional players.
  
  Are there known board games left where a human can still win against an AI? I mean based on the final outcome of authoritative famous matches, where there is still the same board game where AI cannot beat a world champion of that game.
  
  **Tags:** reference-request, game-theory, history, state-of-the-art, board-games
  > **Comments:**
  > **username_2**: Well, games like Ludo are almost entirely based on luck. So there's a change of about 50% that a human would win from an "AI" in this case.  
  > **username_1**: Can't a human + computer still beat any AI system in these games?  Keeping overall computational power the same in both cases, of course.  Kasparov said he staged human vs. AI chess competitions, and amateurs + low powered computers beat experts and supercomputers.  
  
  ---
  
  ## 4 answers
  
  ### Answer by username_5 on 2016-08-12. Score: 6 | Accepted answer
  
  For many years, the focus has been on games with perfect information. That is, in Chess and Go both of us are looking at the same board. In something like Poker, you have information that I don't have and I have information that you don't have, and so for either of us to make sense of each other's actions we need to model what hidden information the other player has, and *also* manage how we leak our hidden information. (A poker bot whose hand strength could be trivially determined from its bets will be easier to beat than a poker bot that doesn't.)
  
  Current research is switching to tackling games with imperfect information. Deepmind, for example, [has said](http://www.businessinsider.com/google-deepmind-could-play-starcraft-2016-3) they might approach Starcraft next.
  
  I don't see too much difference between video games and board games, and there are several good reasons to switch to video games for games with imperfect information.
  
  One is that if you want to beat the best human to be a major victory, there needs to be a pyramid of skill that human is atop of--it'll be harder to unseat the top Starcraft champion than the top Warcraft champion, even though the bots might be comparably difficult to code, just because humans have tried harder at Starcraft.
  
  Another is that many games with imperfect information deal with reading faces and concealing information, which an AI would have an unnatural advantage at; for multiplayer video games, players normally interact with each other through a server as an intermediary and so the competition will be more normal.
  
  
  ---
  
  ### Answer by username_3 on 2016-08-11. Score: 8
  
  Not all games (or even board games) are computationally algorithmic. Even the least skilled player is likely to trounce the hottest pattern-matching algorithm in a game of **[Pictionary](https://en.wikipedia.org/wiki/Pictionary)** (for example).
  
  ![](https://i.sstatic.net/RDIuC.png)
  
  If you want to say that the movement of pieces upon successful completion of a task is only ancelary to the object of the game, than your answer will be largely self-selecting. A sufficiently sophisticated algorithm will brute force a computational problem better than human intuition… [**eventually.**](https://en.wikipedia.org/wiki/AI_effect)
  
  > **Comments:**
  > **username_4**: Citation needed.  
  > **username_5**: Yeah, I think Pictionary might be pretty easy to beat these days. Since it's team-based it's not clear what a human vs. computer matchup would look like, though.  
  
  ---
  
  ### Answer by username_6 on 2016-08-11. Score: 2
  
  Artificially intelligent computer programs should be able to be at the same level or beat humans at every game that we play. This is because games follow rules that are scriptable, and [artificial intelligence](http://www.aaai.org/ojs/index.php/aimagazine/article/view/2310) is designed to focus on one specific game and learn from its failures. The difference between humans and artificial intelligence is that artificial intelligence focuses on one specific task like learning to master Go while our brain is dedicated to mastering multiple tasks like...living. Even Arimaa, a game designed to be difficult for artificially intelligent systems was beaten by a bot called Sharp: <https://en.wikipedia.org/wiki/Arimaa>.
  
  > **Comments:**
  > **username_4**: Many games have social elements. How do you script those?  
  > **username_6**: This is a good point, but I am not sure I understand.  Many of the games I have played or seen artificial intelligence play have clearly defined rules.  By social, do you mean that the rules can change based on the people playing?  For example, my  cousins play the card game "War" a bit differently as they use the "Joker" card as the highest card while my brother and I don't use that card.  Can you clarify what you mean by social?  
  > **username_4**: In Werewolf, the whole gameplay revolves around people talking and deciding whom to take out; there are barely any rules beyond that. But even *if* you have rules, think of cooperative games like Pandemic; how does the AI *discuss*? What about games that involve moral decisions like e.g. This War of Mine? In summary, I think your claim "computer programs [...] beat humans at every game that we play" is wrong -- precisely because *not* every game follows scriptable rules.  
  
  ---
  
  ### Answer by username_7 on 2024-07-14. Score: 0
  
  In some sense, Go returned to this category in 2023 with the [discovery](https://arxiv.org/pdf/2211.00241) of an inherent weakness in superhuman Go-playing AIs. There is a particular pattern that the AI consistently misinterprets, so forcing that kind of position leads the AI to play blunders that are clear even to a Go novice. Figure J.2 in the paper shows an example. The idea is to create a dead group and let the AI enclose it, but not capture it (which is perfectly correct behaviour). The AI them mistakenly considers the enclosing group unconditionally alive, even though for that it would have to actually capture the dead group. It then lets the enclosing group be captured. The loss involves a misread capture race; the particular blunder move is clear even to a Go novice.
  
  Importantly, though the strategy was discovered through training an adversarial AI, it can be played by an unaided human. Also importantly, this is not a trivially fixable bug. The main author of KataGo confirms this [here](https://www.reddit.com/r/MachineLearning/comments/yjryrd/comment/iuqm4ye/?utm_source=reddit&utm_medium=web2x&context=3), pointing out also that KataGo, a commonly-used superhuman Go AI, sometimes misreads specific positions that arise within regular gameplay when they get out of the space explored in self-play. This means that superhuman Go AIs require various ad hoc additions to their training. The original paper discvered two exploits, one that relied on a particular ruleset and the more legitimate one described above, but it's possible that there are yet more exploits that would be found next if this got patched.
  
  Since there aren't big human-versus-AI events in Go these days, it's hard to evaluate how (un)fixable this problem is, but professional Go players take it seriously. [Here](https://www.youtube.com/watch?v=H4DvCj4ySKM) is a dan-level professional playing the strategy out and reflecting on what it means for the game as a piece of culture.
  
  As an aside, I find this fascinating; it's what got me into Go, through [this essay](https://franklantz.substack.com/p/the-afterlife-of-go) (which I also recommend, even if you don't know Go at all). I learned the game to understand what this kind of exploit means, and I think I've figured it out. It shows one component of human intelligence that we have not replicated so far in AI: humans have a kind of "constant vigilance". A human encountering this strategy would think "this is weird, my enemy is up to something". They would spot where the trap seemed to lie, and play the obvious safeguarding move to block it, even if they never saw anything like it before. I have no idea how humans do this. I guess it shows that intelligence is much more of an ill-defined, ad-hoc, improvisational kind of thing than generally assumed.
  
  
  ---
  
  