SlideShare a Scribd company logo
On anti-cheating in chess,
science, reproducibility, and
variability
Mathieu Acher
Chess fairy tale :
● The Queen's Gambit (Netflix)
● streaming/Youtube
● investments (chess.com, chess24, Play Magnus, lichess, etc.)
● AlphaZero didn’t kill the interest
Cheating threatens Chess fairy tale. Almost
forgotten/hidden, but now getting back…
Cheating threatens Chess fairy tale. How does it work?
Use of chess engines during play.
Chess engines like Stockfish or AlphaZero-like variant can
give to the cheater a decisive advantage, since almost
perfect moves can be played.
Elo rating system: method for calculating
the relative skill levels of each player
Stockfish is 3500+
Magnus Carlsen (world champion) is 2834
Already in 2006, Topalov accused Kramnik as part of the
world championship.
Sébastien Feller
https://en.wikipedia.org/wiki/S%C3%A9bastien_Feller
case in Chess Olympiad in 2010.
In October 2010, Feller scored 6/9 (+5 =2 -2) during the 39th Chess Olympiad and won the Gold
medal for best individual performance on board 5. However, the FFE accused Feller, along with
French players GM Arnaud Hauchard and IM Cyril Marzolo, of cheating during the Olympiad. While
Feller was in the playing hall, Marzolo was in France where he checked the best moves with a
chess computer. Marzolo then allegedly sent the move in coded pairs of numbers by text message
to Hauchard. Once Hauchard had the suggested move, he would position himself in the hall behind
one of the other players’ tables in a predefined coded system, where each table represented a
move to play. The FFE claims, in all, 200 text messages were sent during the tournament. The
scam was supposedly uncovered by FFE vice-president Joanna Pomian
Sébastien Feller
https://en.wikipedia.org/wiki/S%C3%A9bastien_Feller
case in Chess Olympiad in 2010.
Not guilty ;-)
With the rise of online games and $$$, cheating is even
more problematic.
A few weeks ago, Magnus Carlsen
https://en.wikipedia.org/wiki/Magnus_Carlsen accused
Hans Niemann (HN) of cheating over the board and
refused to ever play him again.
During the Sinquefield Cup in September 2022, a controversy arose between
chess grandmasters Magnus Carlsen, the current world champion, and Hans
Niemann. Carlsen, after surprisingly losing in their matchup, dropped out of the
tournament. Many interpreted his withdrawal as an insinuation of an accusation
that Niemann cheated. In their next tournament meetup, Carlsen abruptly resigned
after one move, perplexing observers again. It became the most serious scandal
about cheating allegations for international chess in years and garnered significant
attention in the news media worldwide.
You may have heard headlines with "anal bead" supposed
to help HN.
I'm not specifically aiming to talk about chess (and
cheating).
I rather want to discuss how science has been (and will
be) at the heart of the anti-cheating chess problem. It’s an
interesting case.
How to detect cheaters?
The basic idea is to confront moves played by humans (players) with those of
computer engines. The more you play like a computer…
How to detect cheaters?
The basic idea is to confront moves played by humans (players) with those of
computer engines. The more you play like a computer…
How to detect cheaters?
The basic idea is to confront moves played by humans (players) with those of
computer engines. The more you play like a computer…
Rigorous methods with backed up claims
vs
Fancy analysis that can ruin life of people
I will first argue that many people (chess
hobbyists/experts, data nerds, etc.), most being
non-scientists, have actually done science for trying to
demonstrate or refute the cheating case.
I will first argue that many people (chess
hobbyists/experts, data nerds, etc.), most being
non-scientists, have actually done science for trying to
demonstrate or refute the cheating case.
With the sharing of data (analysis of chess games like
those played by HN), scripts, and methods, numerous
results and conclusions have emerged, getting popularized
with social media (twitch, Youtube, twitter, etc.)
On the one hand, I've been quite excited to see all this
energy for trying to advance our understanding and
propose interesting ideas/analysis.
On the other hand, there have been some failures in the quality of some analysis or the choice of closed
systems to compute unclear metrics:
● Let’s Check Analysis is based on a proprietary system: undefined, opaque, non-reproducible
● Dubious combination of probabilities
● More comparisons needed with other players... too easy to find a "pattern" on HN, and then compare
to one player and data point!
● Analysis sensitive to depth and engine
In-between, there have been a report by chess.com and the analysis of the computer scientist Ken Regan
https://cse.buffalo.edu/~regan/ the world renewed specialist.
I still think the problem is open (eg Regan's method is too conservative and
missing many cases; chess.com methodology, though unclear and opaque at
some points, is certainly effective for online cheating, but not over the board
detection).
I will present a variability model of the space of experiments/methods that can be
considered to address the problem.
Dataset
only HN
games
a few
grandmasters for
comparison
Carlsen
Nakamura
…
Rising
stars
Cheaters
all games in the
planet? OTB?
online?
Dataset
Preprocess
consider all
moves
ignore
openings
move
ignore N
first moves
(eg N=8)
use ECO
Encyclopaedia of
Chess Openings
ignore
endgames
“critical”
positions only
(note: define
critical)
Dataset Metrics
ROI
from
Regan
Let’s
Check
Preprocess
Compare with
strength “profile”
(e.g., Elo rating at
that time)
Strength
score
(chess.com)
Dataset Metrics Engine
depth
version
Preprocess
This model can be used to
● pilot the collaborative effort,
● to reproduce, replicate or reject some experiments,
● and to gain confidence or robustness in some conclusions.
Dataset Metrics Engine
depth
version
Preprocess
28
different
methods
different
assumptions
different analyses
different data
the obvious: OPEN, reproducible
open in a security context?
Haematocrit in cyclism (EPO; <= 50 in 2000s’)
Goodhart's law "When a measure becomes a target, it ceases to be a good
measure"
ROI from Regan is unclear/not well-defined… why? perhaps because ROI can be
then systematically computed, monitored, and thus optimized by cheaters
Another last point I want to discuss is that most probably the anti-cheating chess
problem cannot be resolved solely with retrospective computational analysis.
It's just too uncertain, especially if cheaters are "smart".
(Cyber-)security experts, psychologists, chess players, and of course computer
science nerds/professionals can contribute to address this multidisciplinary
problem.

More Related Content

More from University of Rennes, INSA Rennes, Inria/IRISA, CNRS

A Demonstration of End-User Code Customization Using Generative AI
A Demonstration of End-User Code Customization Using Generative AIA Demonstration of End-User Code Customization Using Generative AI
A Demonstration of End-User Code Customization Using Generative AI
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)
24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)
24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
On Programming Variability with Large Language Model-based Assistant
On Programming Variability with Large Language Model-based AssistantOn Programming Variability with Large Language Model-based Assistant
On Programming Variability with Large Language Model-based Assistant
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Generative AI for Reengineering Variants into Software Product Lines: An Expe...
Generative AI for Reengineering Variants into Software Product Lines: An Expe...Generative AI for Reengineering Variants into Software Product Lines: An Expe...
Generative AI for Reengineering Variants into Software Product Lines: An Expe...
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Tackling Deep Software Variability Together
Tackling Deep Software Variability TogetherTackling Deep Software Variability Together
Tackling Deep Software Variability Together
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...
Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...
Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Machine Learning and Deep Software Variability
Machine Learning and Deep Software VariabilityMachine Learning and Deep Software Variability
Machine Learning and Deep Software Variability
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Mastering Software Variability for Innovation and Science
Mastering Software Variability for Innovation and ScienceMastering Software Variability for Innovation and Science
Mastering Software Variability for Innovation and Science
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size
Transfer Learning Across Variants and Versions: The Case of Linux Kernel SizeTransfer Learning Across Variants and Versions: The Case of Linux Kernel Size
Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Reproducible Science and Deep Software Variability
Reproducible Science and Deep Software VariabilityReproducible Science and Deep Software Variability
Reproducible Science and Deep Software Variability
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Software Variability and Artificial Intelligence
Software Variability and Artificial IntelligenceSoftware Variability and Artificial Intelligence
Software Variability and Artificial Intelligence
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Teaching Software Product Lines: A Snapshot of Current Practices and Challenges
Teaching Software Product Lines: A Snapshot of Current Practices and ChallengesTeaching Software Product Lines: A Snapshot of Current Practices and Challenges
Teaching Software Product Lines: A Snapshot of Current Practices and Challenges
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...
Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...
Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Assessing Product Line Derivation Operators Applied to Java Source Code: An E...
Assessing Product Line Derivation Operators Applied to Java Source Code: An E...Assessing Product Line Derivation Operators Applied to Java Source Code: An E...
Assessing Product Line Derivation Operators Applied to Java Source Code: An E...
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Synthesis of Attributed Feature Models From Product Descriptions
Synthesis of Attributed Feature Models From Product DescriptionsSynthesis of Attributed Feature Models From Product Descriptions
Synthesis of Attributed Feature Models From Product Descriptions
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
From Basic Variability Models to OpenCompare.org
From Basic Variability Models to OpenCompare.orgFrom Basic Variability Models to OpenCompare.org
From Basic Variability Models to OpenCompare.org
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Pandoc: a universal document converter
Pandoc: a universal document converterPandoc: a universal document converter
Pandoc: a universal document converter
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
Metamorphic Domain-Specific Languages
Metamorphic Domain-Specific LanguagesMetamorphic Domain-Specific Languages
Metamorphic Domain-Specific Languages
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
3D Printing, Customization, and Product Lines
3D Printing, Customization, and Product Lines3D Printing, Customization, and Product Lines
3D Printing, Customization, and Product Lines
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 
WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)
WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)
WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)
University of Rennes, INSA Rennes, Inria/IRISA, CNRS
 

More from University of Rennes, INSA Rennes, Inria/IRISA, CNRS (20)

A Demonstration of End-User Code Customization Using Generative AI
A Demonstration of End-User Code Customization Using Generative AIA Demonstration of End-User Code Customization Using Generative AI
A Demonstration of End-User Code Customization Using Generative AI
 
24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)
24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)
24 Reasons Why Variability Models Are Not Yet Universal (24RWVMANYU)
 
On Programming Variability with Large Language Model-based Assistant
On Programming Variability with Large Language Model-based AssistantOn Programming Variability with Large Language Model-based Assistant
On Programming Variability with Large Language Model-based Assistant
 
Generative AI for Reengineering Variants into Software Product Lines: An Expe...
Generative AI for Reengineering Variants into Software Product Lines: An Expe...Generative AI for Reengineering Variants into Software Product Lines: An Expe...
Generative AI for Reengineering Variants into Software Product Lines: An Expe...
 
Tackling Deep Software Variability Together
Tackling Deep Software Variability TogetherTackling Deep Software Variability Together
Tackling Deep Software Variability Together
 
Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...
Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...
Feature Subset Selection for Learning Huge Configuration Spaces: The case of ...
 
Machine Learning and Deep Software Variability
Machine Learning and Deep Software VariabilityMachine Learning and Deep Software Variability
Machine Learning and Deep Software Variability
 
Mastering Software Variability for Innovation and Science
Mastering Software Variability for Innovation and ScienceMastering Software Variability for Innovation and Science
Mastering Software Variability for Innovation and Science
 
Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size
Transfer Learning Across Variants and Versions: The Case of Linux Kernel SizeTransfer Learning Across Variants and Versions: The Case of Linux Kernel Size
Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size
 
Reproducible Science and Deep Software Variability
Reproducible Science and Deep Software VariabilityReproducible Science and Deep Software Variability
Reproducible Science and Deep Software Variability
 
Software Variability and Artificial Intelligence
Software Variability and Artificial IntelligenceSoftware Variability and Artificial Intelligence
Software Variability and Artificial Intelligence
 
Teaching Software Product Lines: A Snapshot of Current Practices and Challenges
Teaching Software Product Lines: A Snapshot of Current Practices and ChallengesTeaching Software Product Lines: A Snapshot of Current Practices and Challenges
Teaching Software Product Lines: A Snapshot of Current Practices and Challenges
 
Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...
Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...
Exploiting the Enumeration of All Feature Model Configurations: A New Perspec...
 
Assessing Product Line Derivation Operators Applied to Java Source Code: An E...
Assessing Product Line Derivation Operators Applied to Java Source Code: An E...Assessing Product Line Derivation Operators Applied to Java Source Code: An E...
Assessing Product Line Derivation Operators Applied to Java Source Code: An E...
 
Synthesis of Attributed Feature Models From Product Descriptions
Synthesis of Attributed Feature Models From Product DescriptionsSynthesis of Attributed Feature Models From Product Descriptions
Synthesis of Attributed Feature Models From Product Descriptions
 
From Basic Variability Models to OpenCompare.org
From Basic Variability Models to OpenCompare.orgFrom Basic Variability Models to OpenCompare.org
From Basic Variability Models to OpenCompare.org
 
Pandoc: a universal document converter
Pandoc: a universal document converterPandoc: a universal document converter
Pandoc: a universal document converter
 
Metamorphic Domain-Specific Languages
Metamorphic Domain-Specific LanguagesMetamorphic Domain-Specific Languages
Metamorphic Domain-Specific Languages
 
3D Printing, Customization, and Product Lines
3D Printing, Customization, and Product Lines3D Printing, Customization, and Product Lines
3D Printing, Customization, and Product Lines
 
WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)
WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)
WebFML: Synthesizing Feature Models Everywhere (@ SPLC 2014)
 

Recently uploaded

20240520 Planning a Circuit Simulator in JavaScript.pptx
20240520 Planning a Circuit Simulator in JavaScript.pptx20240520 Planning a Circuit Simulator in JavaScript.pptx
20240520 Planning a Circuit Simulator in JavaScript.pptx
Sharon Liu
 
Phenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvementPhenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvement
IshaGoswami9
 
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills MN
 
molar-distalization in orthodontics-seminar.pptx
molar-distalization in orthodontics-seminar.pptxmolar-distalization in orthodontics-seminar.pptx
molar-distalization in orthodontics-seminar.pptx
Anagha Prasad
 
Shallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptxShallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptx
Gokturk Mehmet Dilci
 
What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.
moosaasad1975
 
The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defectsThe binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
Sérgio Sacani
 
aziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobelaziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobel
İsa Badur
 
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
Sérgio Sacani
 
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptxBREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
RASHMI M G
 
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Sharlene Leurig - Enabling Onsite Water Use with Net Zero WaterSharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Texas Alliance of Groundwater Districts
 
Randomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNERandomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNE
University of Maribor
 
Applied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdfApplied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdf
University of Hertfordshire
 
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
University of Maribor
 
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
University of Maribor
 
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
yqqaatn0
 
Bob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdfBob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdf
Texas Alliance of Groundwater Districts
 
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốtmô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
HongcNguyn6
 
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
Abdul Wali Khan University Mardan,kP,Pakistan
 
Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdf
Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdfTopic: SICKLE CELL DISEASE IN CHILDREN-3.pdf
Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdf
TinyAnderson
 

Recently uploaded (20)

20240520 Planning a Circuit Simulator in JavaScript.pptx
20240520 Planning a Circuit Simulator in JavaScript.pptx20240520 Planning a Circuit Simulator in JavaScript.pptx
20240520 Planning a Circuit Simulator in JavaScript.pptx
 
Phenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvementPhenomics assisted breeding in crop improvement
Phenomics assisted breeding in crop improvement
 
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...
 
molar-distalization in orthodontics-seminar.pptx
molar-distalization in orthodontics-seminar.pptxmolar-distalization in orthodontics-seminar.pptx
molar-distalization in orthodontics-seminar.pptx
 
Shallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptxShallowest Oil Discovery of Turkiye.pptx
Shallowest Oil Discovery of Turkiye.pptx
 
What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.What is greenhouse gasses and how many gasses are there to affect the Earth.
What is greenhouse gasses and how many gasses are there to affect the Earth.
 
The binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defectsThe binding of cosmological structures by massless topological defects
The binding of cosmological structures by massless topological defects
 
aziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobelaziz sancar nobel prize winner: from mardin to nobel
aziz sancar nobel prize winner: from mardin to nobel
 
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...
 
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptxBREEDING METHODS FOR DISEASE RESISTANCE.pptx
BREEDING METHODS FOR DISEASE RESISTANCE.pptx
 
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Sharlene Leurig - Enabling Onsite Water Use with Net Zero WaterSharlene Leurig - Enabling Onsite Water Use with Net Zero Water
Sharlene Leurig - Enabling Onsite Water Use with Net Zero Water
 
Randomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNERandomised Optimisation Algorithms in DAPHNE
Randomised Optimisation Algorithms in DAPHNE
 
Applied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdfApplied Science: Thermodynamics, Laws & Methodology.pdf
Applied Science: Thermodynamics, Laws & Methodology.pdf
 
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...
 
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...
 
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
如何办理(uvic毕业证书)维多利亚大学毕业证本科学位证书原版一模一样
 
Bob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdfBob Reedy - Nitrate in Texas Groundwater.pdf
Bob Reedy - Nitrate in Texas Groundwater.pdf
 
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốtmô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
mô tả các thí nghiệm về đánh giá tác động dòng khí hóa sau đốt
 
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...THEMATIC  APPERCEPTION  TEST(TAT) cognitive abilities, creativity, and critic...
THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
 
Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdf
Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdfTopic: SICKLE CELL DISEASE IN CHILDREN-3.pdf
Topic: SICKLE CELL DISEASE IN CHILDREN-3.pdf
 

On anti-cheating in chess, science, reproducibility, and variability

  • 1. On anti-cheating in chess, science, reproducibility, and variability Mathieu Acher
  • 2. Chess fairy tale : ● The Queen's Gambit (Netflix) ● streaming/Youtube ● investments (chess.com, chess24, Play Magnus, lichess, etc.) ● AlphaZero didn’t kill the interest
  • 3. Cheating threatens Chess fairy tale. Almost forgotten/hidden, but now getting back…
  • 4. Cheating threatens Chess fairy tale. How does it work? Use of chess engines during play.
  • 5. Chess engines like Stockfish or AlphaZero-like variant can give to the cheater a decisive advantage, since almost perfect moves can be played. Elo rating system: method for calculating the relative skill levels of each player Stockfish is 3500+ Magnus Carlsen (world champion) is 2834
  • 6. Already in 2006, Topalov accused Kramnik as part of the world championship.
  • 7. Sébastien Feller https://en.wikipedia.org/wiki/S%C3%A9bastien_Feller case in Chess Olympiad in 2010. In October 2010, Feller scored 6/9 (+5 =2 -2) during the 39th Chess Olympiad and won the Gold medal for best individual performance on board 5. However, the FFE accused Feller, along with French players GM Arnaud Hauchard and IM Cyril Marzolo, of cheating during the Olympiad. While Feller was in the playing hall, Marzolo was in France where he checked the best moves with a chess computer. Marzolo then allegedly sent the move in coded pairs of numbers by text message to Hauchard. Once Hauchard had the suggested move, he would position himself in the hall behind one of the other players’ tables in a predefined coded system, where each table represented a move to play. The FFE claims, in all, 200 text messages were sent during the tournament. The scam was supposedly uncovered by FFE vice-president Joanna Pomian
  • 9. With the rise of online games and $$$, cheating is even more problematic.
  • 10. A few weeks ago, Magnus Carlsen https://en.wikipedia.org/wiki/Magnus_Carlsen accused Hans Niemann (HN) of cheating over the board and refused to ever play him again. During the Sinquefield Cup in September 2022, a controversy arose between chess grandmasters Magnus Carlsen, the current world champion, and Hans Niemann. Carlsen, after surprisingly losing in their matchup, dropped out of the tournament. Many interpreted his withdrawal as an insinuation of an accusation that Niemann cheated. In their next tournament meetup, Carlsen abruptly resigned after one move, perplexing observers again. It became the most serious scandal about cheating allegations for international chess in years and garnered significant attention in the news media worldwide.
  • 11. You may have heard headlines with "anal bead" supposed to help HN.
  • 12. I'm not specifically aiming to talk about chess (and cheating). I rather want to discuss how science has been (and will be) at the heart of the anti-cheating chess problem. It’s an interesting case.
  • 13. How to detect cheaters? The basic idea is to confront moves played by humans (players) with those of computer engines. The more you play like a computer…
  • 14. How to detect cheaters? The basic idea is to confront moves played by humans (players) with those of computer engines. The more you play like a computer…
  • 15. How to detect cheaters? The basic idea is to confront moves played by humans (players) with those of computer engines. The more you play like a computer… Rigorous methods with backed up claims vs Fancy analysis that can ruin life of people
  • 16. I will first argue that many people (chess hobbyists/experts, data nerds, etc.), most being non-scientists, have actually done science for trying to demonstrate or refute the cheating case.
  • 17. I will first argue that many people (chess hobbyists/experts, data nerds, etc.), most being non-scientists, have actually done science for trying to demonstrate or refute the cheating case.
  • 18. With the sharing of data (analysis of chess games like those played by HN), scripts, and methods, numerous results and conclusions have emerged, getting popularized with social media (twitch, Youtube, twitter, etc.)
  • 19. On the one hand, I've been quite excited to see all this energy for trying to advance our understanding and propose interesting ideas/analysis. On the other hand, there have been some failures in the quality of some analysis or the choice of closed systems to compute unclear metrics: ● Let’s Check Analysis is based on a proprietary system: undefined, opaque, non-reproducible ● Dubious combination of probabilities ● More comparisons needed with other players... too easy to find a "pattern" on HN, and then compare to one player and data point! ● Analysis sensitive to depth and engine In-between, there have been a report by chess.com and the analysis of the computer scientist Ken Regan https://cse.buffalo.edu/~regan/ the world renewed specialist.
  • 20. I still think the problem is open (eg Regan's method is too conservative and missing many cases; chess.com methodology, though unclear and opaque at some points, is certainly effective for online cheating, but not over the board detection). I will present a variability model of the space of experiments/methods that can be considered to address the problem.
  • 21. Dataset only HN games a few grandmasters for comparison Carlsen Nakamura … Rising stars Cheaters all games in the planet? OTB? online?
  • 22. Dataset Preprocess consider all moves ignore openings move ignore N first moves (eg N=8) use ECO Encyclopaedia of Chess Openings ignore endgames “critical” positions only (note: define critical)
  • 23. Dataset Metrics ROI from Regan Let’s Check Preprocess Compare with strength “profile” (e.g., Elo rating at that time) Strength score (chess.com)
  • 25.
  • 26.
  • 27. This model can be used to ● pilot the collaborative effort, ● to reproduce, replicate or reject some experiments, ● and to gain confidence or robustness in some conclusions. Dataset Metrics Engine depth version Preprocess
  • 29. the obvious: OPEN, reproducible open in a security context? Haematocrit in cyclism (EPO; <= 50 in 2000s’) Goodhart's law "When a measure becomes a target, it ceases to be a good measure" ROI from Regan is unclear/not well-defined… why? perhaps because ROI can be then systematically computed, monitored, and thus optimized by cheaters
  • 30. Another last point I want to discuss is that most probably the anti-cheating chess problem cannot be resolved solely with retrospective computational analysis. It's just too uncertain, especially if cheaters are "smart". (Cyber-)security experts, psychologists, chess players, and of course computer science nerds/professionals can contribute to address this multidisciplinary problem.