Term Paper: AI and its Affect on Competitive Games
Mount Royal University
GNED-1203: Nature and Ideas
Domenico Rosi
December 11, 2023
Introduction
AI has made very large advancements over the past few decades. As AI technology advances to a
point where it can beat top level professionals in various strategy games such as Go, is it possible
to recognize when an AI has acted in a way that could be considered an error or mistake? Will AI
get to a point where it will never make any mistakes? Is it ever truly possible to know whether
any given AI has made a mistake? Reverse engineering AI to that degree makes it quite difficult
to be able to discern whether what the AI is doing is optimal or not, and so it is hard to be able to
comprehend its full potential. Is no AI calculation a mistake? Is everything truly just a formula?
These questions are intriguing and yet terrifying because they make us feel as though we are
insignificant. That the means in which we think and make decisions in the games we play are not
individualized, rather each action and reaction can be made into a cold, hard, calculation and that
every single move on a chessboard or Go board, every second on the football field, every frame
of data in a fighting game can be made into data that can then be solved. Therefore, does this also
mean that players do not have playstyles that are distinct? Are they simply just flawed in
different distinct areas and concepts? If a Go player is considered an aggressive player, are they
simply flawed in that they aren’t defensive enough? AI relies on analyzing and interpreting data,
and in the context of playing a game, this means finding the most efficient means of making a
move within a given set of parameters and permutations. This also means that the more
processing power an AI has, the more permutations and variations it can analyze and solve. So, if
an AI had enough processing power to analyze every single permutation, does that mean that an
AI could achieve perfection in any given game, or even scarier, any given medium?
How about games that have entirely different characters and playstyles? Classic competitive
games like Super Smash Brothers Melee, which have had a competitive scene for decades now,
are still yet to have developed a stagnant meta. In competitive games, the meta or metagame is
the current trends that define what characters, strategies, and playstyles are considered optimal.
But in the context of Melee, over the twenty years in which individual players have been
competing for the title of “best in the world”, what has been considered the meta has been
constantly changing. Five years ago, for example, Yoshi was considered a very bad character, as
he lacks movement and defensive options that most other characters have. However, in what was
considered a highlight underdog story in these competitive circles, a Japanese Yoshi player
named Masaya “aMSa” Chikamoto, who has been competing in America since 2013, won The
Big House 10, which was considered the most stacked tournament in Melee history. A bracket
that had what many considered the largest turnout of top-level talent to have ever competed in
one event, during the weekend of October 7th to the 9th. This win also made him the first person
ever to win a major with Yoshi. He was and still is the only top 100 player in the world who
plays Yoshi, and only Yoshi. He then went on to continue to win multiple different majors after,
ranking as the 2nd highest player in the world during the annual ranking for 2022.
Does A.I. see what we cannot?
If it took twenty years for a player to discover the viability of a certain character, theoretically, an
AI that can calculate all the permutations and combinations that could ever possibly exist would
also be able to recognize the hidden potential of characters that many others have overlooked.
Twenty years of a developing meta, simply analyzed and recognized in seconds due to the sheer
processing capability of such an AI. Of course, this is not possible in the modern day or in the
far-off future. The sheer amount of energy required to power such an AI would require quantum
computing and even then, would require too much energy to be feasible. However, it is worth
considering that AlphaGo, which beat top level players Ke Jie and Lee Sedol in the classic game
of Go, have been described as making very unorthodox opening moves (Chan, 2017). Yet these
moves, as incomprehensible as they may seem, have then been copied reused by other
professional Go players, albeit to less consistent results (Chan, 2017). If AI can commit to game
strategies that top level Go players cannot even conceive, this potentially means that AI can
create entirely new strategies and playstyles that we have yet to observe. No one understood why
aMSa chose Yoshi (outside of sheer love for the character), until they observed how he utilized
the hidden strengths of Yoshi through a combination of aMSa’s high level of technical
understanding of the character and his insane reaction skills. Just as he developed a new strategic
playstyle out of thin air, so too does AI. It isn’t necessarily impossible to find these new
strategies on our own, but AI can find those solutions because it can predict different and specific
outcomes by calculating different permutations and combinations.
How does AI achieve epistemological understanding in games, and how do we affect that?
The way AI achieves understanding cannot be generalized in one singular way, as different AI
are built to achieve different things and as such are programmed and adapt in different ways.
When DeepMind set out to program AI to be able to play and the entirety of the Atari 2600
library, they developed an AI software called Deep Q Networks (Christian, 2022). This AI was
able to outscore professional game testers by huge margins, sometimes scoring up to twenty to
thirty times better than their human counterparts. However, there was one game that this AI
struggled with, that being a puzzle adventure game, Montezuma's Revenge. In this game, the
player must collect keys and avoid pitfalls to collect points. Much unlike other Atari games, the
way in which you score points in the game is not linear and requires many different steps.
Because of this, Deep Q failed to get past the first screen because it was not able to recognize
what it was doing wrong, since the entire incentive structure was based on scoring points. Rather
than collecting the key and collecting points, it kept trying random actions and dying. It was only
after the programmers added another new incentive structure to the AI that it was able to
progress. Novel experiences (Christian, 2022).
Just as babies are more prone to look a novel objects, experiences, and ideas, the AI needed to be
able to do the same. By giving the AI a preference towards new and different screens and
outcomes, the AI was able to finally score points by grabbing the key, and with this new
incentive parameter, was able to continue exploring a large portion of the game (Christian,
2022).
It is reasonable to say then, that AI learns through the means in which we learn. The difference
being in that AI has more processing power to be able to predict outcomes in comparison to
humans. However, the parameters we give AI leave it prone to failure. While human beings are
prebuilt with the capacity for curiosity and ingenuity, AI can only work in the parameters we
allow them to. This is indeed a strange dichotomy. The fact that, when given the parameters and
information necessary to learn a complex game, AI can absolutely overtake and beat top level
players, yet simultaneously they only can do so because we as humans gave them a set number
of parameters to use as incentives or dissuasion. AI learns what we want AI to learn. AI does not
act on its own, it is entirely influenced by what we want out of any given AI.
Everything done by an AI is a calculation.
This statement is true. This may be terrifying to those who see AI as infallible, but this could not
be further from the truth. When given the proper parameters to be able to achieve the task a given
AI is set to carry out, AI is indeed more efficient than humans. However, the calculations that an
AI undertakes are not impervious to human failure. Because ultimately, the calculations that an
AI undertakes are bound by human parameters. As such, if the parameters that we give an AI are
not sufficient, the AI will falter in its ability complete its task. However, this understanding leads
to another interesting realization. If AI is entirely built on parameters prone to human failure,
then the prospect of a “perfect AI” must be impossible, or at least extremely unlikely. Because AI
are, in essence, human ideas turned into code and data processing algorithms, for an AI to
“solve” a complex strategy game like Go means that the human programmers behind the
program must have considered every single parameter that can affect the outcome of a game. The
problem then becomes that…
…modern AI can solve our definition of a game, but not the game itself.
This applies to all aspects of any AI, whether it is playing a game, writing news articles, creating
prompts for students, or programming blocks of code. AI can solve our own definition of a
problem, but it cannot solve the very essence of a given problem. The flaw lies not in the
processing of the calculation, but the definition of what an acceptable “calculation” is. As AI can
calculate what we wish for it, we are not able to fully confirm whether our own parameters are
perfect. The mistakes the AI makes, in Go, or Chess, or in scraping data off the internet, is not
that the AI itself makes the mistake. What AI may consider an acceptable answer to a question is
one that is not dictated by itself, but what we dictate the AI need consider acceptable. This
definition is entirely mathematical, but outside of the realm of mathematics, this isn’t enough.
Acceptable artistic expression, and plagiarism.
To highlight how these parameters we give to AI are not sufficient to consider them perfect, we
need look no further than the sheer amount of plagiarised content AI tends to use. Artist Kelly
McKernan noticed in 2022 that her name was being used in image prompts for Midjourney, an
image generation AI (Chayka, 2023). A year later, she filed class action lawsuits against three
different AI image generators; Midjourney, StableDiffusion, and DreamUp. All three generators
use laion-5B, a non-profit internet database that logs between five and six billion images from
the internet, including that of artists (Chayka, 2023). Though this lawsuit is still pending, and
therefore the legal consequences of the lawsuit are yet to be put on display, the moral
consequences of unchecked generative AI are made clear. Plagiarising the work of other artists to
feed data algorithms. What caused this of course was the parameters that AI acted under (or lack
thereof). There were no parameters that allowed for the AI to check any given piece of media for
royalties. The images themselves are beautiful. Canvases of artistic work that look more and
more like human artists every single day. The definition of what we consider a beautiful painting,
in this way, has been fulfilled. However, the error again lies in the parameters. Sure, the paintings
are indeed beautiful, but from whom did the AI pull from to create a convincing work of art?
But does this same logic apply to games?
AI does not have the processing power to calculate every single permutation and combination
possible in any given game. This means that the AI must not only consider a limited number of
permutations and combinations, but it needs to calculate which permutations and combinations it
should process. With this limited amount of information, the AlphaGo AI may be able to
consider many more permutations and combinations than the greatest players, but not all of
them. The AI must calculate which permutations to consider before it then processes the data.
But this of course assumes that the AI does not have infinite processing power. If an AI has
infinite processing power, is it capable of “solving” a game?
Games are entirely mathematical.
Games are indeed entirely mathematical, because like AI, they are restricted by set parameters
and means of interaction. If every single outcome can be interpreted and calculated, and there is
a way to play these games in a way with no errors or mistakes, then yes. An AI with infinite
processing power should be able to theoretically beat everyone and anyone it encounters with
this perfect formula for the game, whether it be human or a lesser AI. It is here we can make a
clear distinction between a mathematical process like a game and artistic means of expression
like a painting. If the medium has set parameters, such as moves on a board, frame data and
positions in a Smash Bros Game, or the probability of a deck of cards, then because
epistemologically, AI can only understand and act within a set range of parameters, there is
indeed a perfect way for a game to be solved. However, when it comes to an artistic mode of
expression, then no. There is no such thing as an “incorrect” or “erroneous” artistic piece. AI
may be able to replicate art, but there is no such thing as a calculation for a beautiful painting
because art is subjective. It is not limited by set parameters. Art has infinite possibilities, and
therefore cannot be perfected by an infinite amount of processing power. But what else could be
calculated by an AI with infinite processing power?
The universe is not subjective, it too can be calculated.
If an AI with infinite processing power can solve any sort of problem that falls under entirely
mathematical parameters, then by this logic, the Theory of Everything could potentially be
calculated by an AI. This far off idea is one that scientists are still considering right now. At the
Institute for Artificial Intelligence and Fundamental Interactions, headed by Dr. Max Tegmark,
an experiment was conducted using AI, in which the researchers present fed a neural network
data which involved multiple rockets flying. The neural network was then able to calculate the
essential formulas for the laws of motion (Overbye, 2020). However, these advancement are still
far off from calculating the universe itself. Even with the advancements made in quantum
computing, Dr Tegmark himself admits that there is still much to be done; AI has yet to calculate
advanced concepts like quantum mechanics and relativity. Even the game of life can be
calculated.
Does AI ruin competitive games?
For players like aMSa, who have discovered new and intuitive ways to play old video games,
will the rise of AI kill the potential for new and exciting playstyles to develop? In the case of
chess, Vladimir Kramnik, who is a former competitive chess player, he agrees. Because of AI, he
believes, the potential for new strategies and openings has been dulled and are often played from
just memory rather than logic and intuition. “You don’t even play your own preparation; you
play your computer’s preparation (Simonite, 2023).” However, all is not lost. Kramnik also
believes that by altering the rules of the game itself, AI and mankind can work hand and hand to
find new and exciting strategies in new formats of the original game (Simonite, 2023).
By altering the rules of chess, for example by making it so that castling is illegal, or by making it
so that players are allowed to capture their own pieces, new, exciting ways of rediscovering the
game can be learned and appreciated. For Kramnik, changing the parameters of the game, and
learning the new ways in which this new game can be played adds a huge amount of depth and
strategy in a game that was long considered solved at a human level by AI (Simonite, 2023). If
the parameters of the game make the game so boring and restrictive as strategies become
memorized and optimized to the point of mundanity, change the parameters themselves, and a
whole new game has now been created to be explored and analyzed.
Conclusion
The epistemological effect that AI has on competitive gaming cannot be understated here. AI has
the potential to make some of the complex and interesting strategy games out there boring and
mundane for those who already compete. Every move, every action, becomes data to be
calculated and processed, rather than human plays, that which are more individualized. However,
AI itself is both restricted by the parameters of its own code and the games it plays. By changing
the parameters of which games are played, AI can create infinite new possibilities in the way
games can be played and appreciated. In the case of players like aMSa, who have singlehandedly
developed the meta of an entire character on their own without any help from AI, their craft will
never truly die. AI will never truly replace the triumphant feeling of winning against a real-life
human opponent in a game of merit. Though AI may take the humanity away from optimizing a
strategy, playstyle, or character, it can never take away the adrenaline and excitement that comes
with competition between human competitors. AI may directly affect the optimization and skill
curve that goes into developing a competitive meta, but it will never be able to take away the
competitive spirit of those who play the game. If aMSa was able to singlehandedly change the
metagame of his favorite video game, twenty years after competitions began, then human
ingenuity will always exist in these competitive environments. If not in the game itself, then in
the parameters of which the game is played.
References
Chan, D. (2017). The AI That Has Nothing to Learn from Humans. The Atlantic.
https://www.theatlantic.com/technology/archive/2017/10/alphago-zero-the-ai-that-taughtitself-go/543450/
Christian, B. (2022). Teaching an AI to Beat Video Games Still Takes Human Imagination. Aeon.
https://aeon.co/videos/teaching-an-ai-to-beat-video-games-still-takes-human-imagination
Chayka, K. (2023). Is A.I. Art Stealing from Artists? The New Yorker.
https://www.newyorker.com/culture/infinite-scroll/is-ai-art-stealing-from-artists
Overbye, D. Can a Computer Devise a Theory of Everything? (2020). The New York Times.
https://www.nytimes.com/2020/11/23/science/artificial-intelligence-ai-physicstheory.html
Simonite, T. (2020). AI Ruined Chess. Now, It’s Making the Game Beautiful Again.
https://www.wired.com/story/ai-ruined-chess-now-making-game-beautiful/