Games of Knightian Uncertainty

Spyridon Samothrakis, Dennis J. N. J. Soemers, Damian Machlanski·June 26, 2024

Summary

This paper critiques the declining relevance of games in the context of Artificial General Intelligence (AGI) due to the dominance of Large Language Models (LLMs) and a shift in research focus. It argues that games with Knightian uncertainty, characterized by rapid rule changes and unpredictable environments, could重新position games as crucial testbeds for AGI. The authors point out the limitations of current ML methods, which struggle with generalization and rely on incomplete representations, and propose that games designed to address Knightian uncertainty can help address these issues. They emphasize the need for AI agents to adapt to non-stationary game elements and suggest using chess variants and diverse games for benchmarking generalization and adaptation. The paper advocates for a revised game-based framework that tests for OOD and far-OOD capabilities, arguing that games, when designed to challenge agents, can still contribute to advancing AGI research.

Key findings

1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of games not being effective testbeds for Artificial General Intelligence (AGI) due to the shift in focus towards Large Language Models (LLMs) in the AI community . This problem is not entirely new, as early criticisms by Chollet and Mitchell regarding the limitations of using games for AGI testing still hold . The paper suggests a new approach by proposing "Games of Knightian Uncertainty" as a solution to make game research relevant to the AGI pathway by introducing rapid rule changes as the norm .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that in order for game research to become relevant to the Artificial General Intelligence (AGI) pathway, agents need to be able to adapt to rapid changes in game rules on the fly with no warning, no previous data, and no model access, addressing Knightian uncertainty in the context of games .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes new ideas, methods, and models related to open-ended environment design for multi-agent reinforcement learning. One key concept introduced is the creation of benchmarks to establish measurable objectives for research in games, building upon General Game Playing (GGP) . These benchmarks involve participants being given sets of games, such as variations of chess, and asked to generalize to different variations of the same game or completely different games, termed "near OOD" and "far OOD" steps, respectively . The paper suggests evaluating agents by exposing them to a limited set of demo games for their new setup without providing additional information, following the observation-action paradigm of AI-GYM with observations and actions of arbitrary size .

Furthermore, the paper presents specific examples of setups for training and testing in games like chess, where players learn the fundamentals through a training set representing the standard version of the game, and are then tested on variations like Chess 960 and other chess variants with alternative board sizes or pieces . The concept of transitioning from near OOD chess variants to far OOD games like poker and backgammon is highlighted as a way to explore generalization capabilities . This approach aims to leverage language, communication through stories, generalization capacity, and causality to advance research in open-ended environment design for multi-agent reinforcement learning . The paper introduces the concept of open-ended environment design for multi-agent reinforcement learning, emphasizing benchmarks to measure research progress in games . One key characteristic is the use of General Game Playing (GGP) benchmarks, where participants are tasked with generalizing to different game variations, termed "near OOD" and "far OOD" steps, to evaluate agents . This approach allows for a structured evaluation of agents' generalization capabilities in diverse game settings, providing a clear framework for assessing performance .

Compared to previous methods, the paper's proposed approach offers a systematic way to train and test agents in various game scenarios, such as transitioning from standard chess to chess variants like Chess 960 and other games like poker and backgammon . By incorporating diverse game setups, the method aims to enhance agents' adaptability and generalization skills across different environments, enabling them to tackle a broader range of challenges effectively .

Moreover, the paper emphasizes the importance of leveraging language, communication through stories, generalization capacity, and causality to advance research in open-ended environment design for multi-agent reinforcement learning . This holistic approach not only enhances agents' performance in games but also contributes to the broader goal of developing intelligent systems capable of handling complex and diverse tasks efficiently .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of artificial intelligence and game-playing agents, there are several noteworthy researchers and related researches mentioned in the provided context . Some of the notable researchers include:

  • Y. Duan
  • V. Pogrebniak
  • S. Levine
  • K. O. Stanley
  • J. Lehman
  • L. Soros
  • M. Samvelyan
  • A. Khan
  • M. D. Dennis
  • J. Parker-Holder
  • J. N. Foerster
  • R. Raileanu
  • T. Rockt¨aschel
  • B. J. Loasby
  • K. J. Friston
  • K. E. Stephan
  • M. Hutter
  • J. Richens
  • T. Everitt
  • D. Perez-Liebana
  • S. Samothrakis
  • J. Togelius
  • T. Schaul
  • S. M. Lucas
  • A. Cou¨etoux
  • J. Lee
  • C.-U. Lim
  • T. Thompson
  • H. Finnsson
  • Y. Bj¨ornsson
  • M. Stephenson
  • E. Piette
  • D. J. N. J. Soemers
  • C. Browne
  • J. Hern´andez-Orallo
  • R. Wang
  • A. Rawal
  • J. Zhi
  • Y. Li
  • J. Clune
  • O. Lockwood
  • M. Si
  • M. Jiang
  • T. Rockt¨aschel
  • E. Grefenstette
  • A. Turner
  • L. Smith
  • R. Shah
  • A. Critch
  • P. Tadepalli
  • D. Hupkes
  • V. Dankers
  • M. Mul .

The key to the solution mentioned in the paper involves exploring open-endedness as a force for discovering intelligence and as a component of artificial intelligence itself . This concept of open-endedness is highlighted as a significant aspect in the research related to artificial intelligence and game-playing agents.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the generalization capacity of models through a set of benchmarks organized to create measurable objectives, building upon General Game Playing (GGP) . The experiments involved two different steps: "near OOD" and "far OOD." In the "near OOD" step, participants were given a set of games, such as variations of chess, to generalize to different variations of the same game. In the "far OOD" step, participants were asked to generalize to completely different games, such as backgammon, without providing any model, making search impossible . The evaluation process included exposing agents to a limited set of demo games for their new setup, with no additional information provided, following the observation-action paradigm of AI-GYM . The experiments aimed to test the ability of agents to generalize to different game scenarios and evaluate their performance in adapting to new and diverse challenges.


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study discusses the performance of different machine learning models like LightGBM and a standard MLP in a simulated board game scenario . Regarding the open-source status of the code, the context does not provide information on whether the code used in the study is open source or not. It primarily focuses on the challenges and outcomes of using modern machine learning methods in scenarios with Knightian uncertainty and non-stationary environments .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable support for the scientific hypotheses that need to be verified. The paper discusses the importance of addressing Knightian uncertainty in the context of games to advance Artificial General Intelligence (AGI) research . The experiments conducted aim to test the adaptability of agents to rapid changes in game rules without prior warning, data, or model access, which is crucial for achieving AGI .

The results of the experiments highlight the challenges faced by modern machine learning methods in generalizing beyond the training data distribution, especially when encountering out-of-distribution (OOD) test sets . The experiments demonstrate that while regressors can successfully generalize within the same distribution, they struggle when faced with data from a different distribution, indicating a limitation in their ability to extrapolate .

Moreover, the paper emphasizes the need for agents to learn robust abstractions rather than simply memorizing vast amounts of data . It points out that representational learning should focus on creating useful abstractions rather than filling a large hashmap-like structure with knowledge, as this approach may lead to agents losing to adversarial examples .

Overall, the experiments and results presented in the paper provide valuable insights into the challenges and limitations faced by current machine learning methods in handling Knightian uncertainty and the importance of developing agents that can adapt to unforeseen changes in game rules, which is essential for progress in AGI research .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Operationalizing progress towards Artificial General Intelligence (AGI)
  • Discussing levels of AGI and the path to achieving AGI
  • Exploring the measure of intelligence
  • Investigating mutual-information representation learning objectives for control in artificial intelligence
  • Comparing memorizing versus understanding in the context of data and knowledge
  • Analyzing the role of free-energy in the brain and its relation to artificial intelligence
  • Examining robust agents learning causal world models

What work can be continued in depth?

To delve deeper into the research on games and uncertainty, further exploration can be done on creating benchmarks for measurable objectives in gaming scenarios . This involves organizing benchmarks to assess the generalization capacity of models through storytelling and language, potentially incorporating causality as a way forward . Additionally, investigating the implications of open-endedness in AI frameworks and the challenges posed by non-stationary environments in reinforcement learning and economics could be a fruitful area for continued study . This includes examining how agents adapt to unexpected events and the limitations of statistical reasoning in dynamic and changing environments .


Introduction
Background
Dominance of Large Language Models (LLMs) in AI research
Shift in focus from games to other domains
Objective
To reevaluate the role of games in AGI development
Highlight the potential of Knightian uncertainty games
Method
Data Collection
Analysis of current AI research trends
Case studies of game-based AI advancements
Data Preprocessing
Identification of limitations in current ML methods
Comparison with human adaptability in games
Knightian Uncertainty in Games and AGI
Definition and Importance
Rapid rule changes and unpredictable environments
Potential for AGI benchmarking
Current ML Limitations
Generalization challenges
Incomplete representations
Knightian Games as a Solution
Designing games for adaptability
Chess variants and diverse games as examples
Game-Based Framework for AGI Assessment
Out-of-Distribution (OOD) and Far-OOD Capabilities
Testing grounds for AI resilience
Challenging agents to promote generalization
Relevance of Game-Based Research
Revitalizing game research in AGI context
Potential for innovation and progress
Conclusion
The need for a revised game-centric approach
Games as a valuable component in AGI development
Future directions for game design and AI research integration
Basic info
papers
artificial intelligence
Advanced features
Insights
What are the characteristics of games with Knightian uncertainty mentioned in the paper?
What is the proposed solution by the authors to improve AI agents' capabilities in non-stationary game environments?
What does the paper critique about the relevance of games in AGI research?
How do current ML methods according to the authors perform in addressing Knightian uncertainty?

Games of Knightian Uncertainty

Spyridon Samothrakis, Dennis J. N. J. Soemers, Damian Machlanski·June 26, 2024

Summary

This paper critiques the declining relevance of games in the context of Artificial General Intelligence (AGI) due to the dominance of Large Language Models (LLMs) and a shift in research focus. It argues that games with Knightian uncertainty, characterized by rapid rule changes and unpredictable environments, could重新position games as crucial testbeds for AGI. The authors point out the limitations of current ML methods, which struggle with generalization and rely on incomplete representations, and propose that games designed to address Knightian uncertainty can help address these issues. They emphasize the need for AI agents to adapt to non-stationary game elements and suggest using chess variants and diverse games for benchmarking generalization and adaptation. The paper advocates for a revised game-based framework that tests for OOD and far-OOD capabilities, arguing that games, when designed to challenge agents, can still contribute to advancing AGI research.
Mind map
Potential for innovation and progress
Revitalizing game research in AGI context
Challenging agents to promote generalization
Testing grounds for AI resilience
Chess variants and diverse games as examples
Designing games for adaptability
Incomplete representations
Generalization challenges
Potential for AGI benchmarking
Rapid rule changes and unpredictable environments
Comparison with human adaptability in games
Identification of limitations in current ML methods
Case studies of game-based AI advancements
Analysis of current AI research trends
Highlight the potential of Knightian uncertainty games
To reevaluate the role of games in AGI development
Shift in focus from games to other domains
Dominance of Large Language Models (LLMs) in AI research
Future directions for game design and AI research integration
Games as a valuable component in AGI development
The need for a revised game-centric approach
Relevance of Game-Based Research
Out-of-Distribution (OOD) and Far-OOD Capabilities
Knightian Games as a Solution
Current ML Limitations
Definition and Importance
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Game-Based Framework for AGI Assessment
Knightian Uncertainty in Games and AGI
Method
Introduction
Outline
Introduction
Background
Dominance of Large Language Models (LLMs) in AI research
Shift in focus from games to other domains
Objective
To reevaluate the role of games in AGI development
Highlight the potential of Knightian uncertainty games
Method
Data Collection
Analysis of current AI research trends
Case studies of game-based AI advancements
Data Preprocessing
Identification of limitations in current ML methods
Comparison with human adaptability in games
Knightian Uncertainty in Games and AGI
Definition and Importance
Rapid rule changes and unpredictable environments
Potential for AGI benchmarking
Current ML Limitations
Generalization challenges
Incomplete representations
Knightian Games as a Solution
Designing games for adaptability
Chess variants and diverse games as examples
Game-Based Framework for AGI Assessment
Out-of-Distribution (OOD) and Far-OOD Capabilities
Testing grounds for AI resilience
Challenging agents to promote generalization
Relevance of Game-Based Research
Revitalizing game research in AGI context
Potential for innovation and progress
Conclusion
The need for a revised game-centric approach
Games as a valuable component in AGI development
Future directions for game design and AI research integration
Key findings
1

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of games not being effective testbeds for Artificial General Intelligence (AGI) due to the shift in focus towards Large Language Models (LLMs) in the AI community . This problem is not entirely new, as early criticisms by Chollet and Mitchell regarding the limitations of using games for AGI testing still hold . The paper suggests a new approach by proposing "Games of Knightian Uncertainty" as a solution to make game research relevant to the AGI pathway by introducing rapid rule changes as the norm .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that in order for game research to become relevant to the Artificial General Intelligence (AGI) pathway, agents need to be able to adapt to rapid changes in game rules on the fly with no warning, no previous data, and no model access, addressing Knightian uncertainty in the context of games .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes new ideas, methods, and models related to open-ended environment design for multi-agent reinforcement learning. One key concept introduced is the creation of benchmarks to establish measurable objectives for research in games, building upon General Game Playing (GGP) . These benchmarks involve participants being given sets of games, such as variations of chess, and asked to generalize to different variations of the same game or completely different games, termed "near OOD" and "far OOD" steps, respectively . The paper suggests evaluating agents by exposing them to a limited set of demo games for their new setup without providing additional information, following the observation-action paradigm of AI-GYM with observations and actions of arbitrary size .

Furthermore, the paper presents specific examples of setups for training and testing in games like chess, where players learn the fundamentals through a training set representing the standard version of the game, and are then tested on variations like Chess 960 and other chess variants with alternative board sizes or pieces . The concept of transitioning from near OOD chess variants to far OOD games like poker and backgammon is highlighted as a way to explore generalization capabilities . This approach aims to leverage language, communication through stories, generalization capacity, and causality to advance research in open-ended environment design for multi-agent reinforcement learning . The paper introduces the concept of open-ended environment design for multi-agent reinforcement learning, emphasizing benchmarks to measure research progress in games . One key characteristic is the use of General Game Playing (GGP) benchmarks, where participants are tasked with generalizing to different game variations, termed "near OOD" and "far OOD" steps, to evaluate agents . This approach allows for a structured evaluation of agents' generalization capabilities in diverse game settings, providing a clear framework for assessing performance .

Compared to previous methods, the paper's proposed approach offers a systematic way to train and test agents in various game scenarios, such as transitioning from standard chess to chess variants like Chess 960 and other games like poker and backgammon . By incorporating diverse game setups, the method aims to enhance agents' adaptability and generalization skills across different environments, enabling them to tackle a broader range of challenges effectively .

Moreover, the paper emphasizes the importance of leveraging language, communication through stories, generalization capacity, and causality to advance research in open-ended environment design for multi-agent reinforcement learning . This holistic approach not only enhances agents' performance in games but also contributes to the broader goal of developing intelligent systems capable of handling complex and diverse tasks efficiently .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of artificial intelligence and game-playing agents, there are several noteworthy researchers and related researches mentioned in the provided context . Some of the notable researchers include:

  • Y. Duan
  • V. Pogrebniak
  • S. Levine
  • K. O. Stanley
  • J. Lehman
  • L. Soros
  • M. Samvelyan
  • A. Khan
  • M. D. Dennis
  • J. Parker-Holder
  • J. N. Foerster
  • R. Raileanu
  • T. Rockt¨aschel
  • B. J. Loasby
  • K. J. Friston
  • K. E. Stephan
  • M. Hutter
  • J. Richens
  • T. Everitt
  • D. Perez-Liebana
  • S. Samothrakis
  • J. Togelius
  • T. Schaul
  • S. M. Lucas
  • A. Cou¨etoux
  • J. Lee
  • C.-U. Lim
  • T. Thompson
  • H. Finnsson
  • Y. Bj¨ornsson
  • M. Stephenson
  • E. Piette
  • D. J. N. J. Soemers
  • C. Browne
  • J. Hern´andez-Orallo
  • R. Wang
  • A. Rawal
  • J. Zhi
  • Y. Li
  • J. Clune
  • O. Lockwood
  • M. Si
  • M. Jiang
  • T. Rockt¨aschel
  • E. Grefenstette
  • A. Turner
  • L. Smith
  • R. Shah
  • A. Critch
  • P. Tadepalli
  • D. Hupkes
  • V. Dankers
  • M. Mul .

The key to the solution mentioned in the paper involves exploring open-endedness as a force for discovering intelligence and as a component of artificial intelligence itself . This concept of open-endedness is highlighted as a significant aspect in the research related to artificial intelligence and game-playing agents.


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the generalization capacity of models through a set of benchmarks organized to create measurable objectives, building upon General Game Playing (GGP) . The experiments involved two different steps: "near OOD" and "far OOD." In the "near OOD" step, participants were given a set of games, such as variations of chess, to generalize to different variations of the same game. In the "far OOD" step, participants were asked to generalize to completely different games, such as backgammon, without providing any model, making search impossible . The evaluation process included exposing agents to a limited set of demo games for their new setup, with no additional information provided, following the observation-action paradigm of AI-GYM . The experiments aimed to test the ability of agents to generalize to different game scenarios and evaluate their performance in adapting to new and diverse challenges.


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study discusses the performance of different machine learning models like LightGBM and a standard MLP in a simulated board game scenario . Regarding the open-source status of the code, the context does not provide information on whether the code used in the study is open source or not. It primarily focuses on the challenges and outcomes of using modern machine learning methods in scenarios with Knightian uncertainty and non-stationary environments .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable support for the scientific hypotheses that need to be verified. The paper discusses the importance of addressing Knightian uncertainty in the context of games to advance Artificial General Intelligence (AGI) research . The experiments conducted aim to test the adaptability of agents to rapid changes in game rules without prior warning, data, or model access, which is crucial for achieving AGI .

The results of the experiments highlight the challenges faced by modern machine learning methods in generalizing beyond the training data distribution, especially when encountering out-of-distribution (OOD) test sets . The experiments demonstrate that while regressors can successfully generalize within the same distribution, they struggle when faced with data from a different distribution, indicating a limitation in their ability to extrapolate .

Moreover, the paper emphasizes the need for agents to learn robust abstractions rather than simply memorizing vast amounts of data . It points out that representational learning should focus on creating useful abstractions rather than filling a large hashmap-like structure with knowledge, as this approach may lead to agents losing to adversarial examples .

Overall, the experiments and results presented in the paper provide valuable insights into the challenges and limitations faced by current machine learning methods in handling Knightian uncertainty and the importance of developing agents that can adapt to unforeseen changes in game rules, which is essential for progress in AGI research .


What are the contributions of this paper?

The paper makes several contributions, including:

  • Operationalizing progress towards Artificial General Intelligence (AGI)
  • Discussing levels of AGI and the path to achieving AGI
  • Exploring the measure of intelligence
  • Investigating mutual-information representation learning objectives for control in artificial intelligence
  • Comparing memorizing versus understanding in the context of data and knowledge
  • Analyzing the role of free-energy in the brain and its relation to artificial intelligence
  • Examining robust agents learning causal world models

What work can be continued in depth?

To delve deeper into the research on games and uncertainty, further exploration can be done on creating benchmarks for measurable objectives in gaming scenarios . This involves organizing benchmarks to assess the generalization capacity of models through storytelling and language, potentially incorporating causality as a way forward . Additionally, investigating the implications of open-endedness in AI frameworks and the challenges posed by non-stationary environments in reinforcement learning and economics could be a fruitful area for continued study . This includes examining how agents adapt to unexpected events and the limitations of statistical reasoning in dynamic and changing environments .

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