AI Game Playing and Rule Following

The development of reliable and effective AI models hinges on their ability to adapt to and follow new rules, a critical skill often tested through game playing. AI systems must demonstrate their capacity to understand and adhere to game rules on the fly, a challenge that reflects their broader potential to mimic human-like intelligence. This process begins with simple games like tic-tac-toe, which, despite their simplicity, contain complex variations that test an AI's rule-following abilities.

Martin Gardner, a renowned figure in the world of mathematical puzzles and games, highlighted the intricacies of even the simplest games. His insights suggest that games like tic-tac-toe can provide valuable lessons for making AI more human-like. Games require imagination, understanding, and strict adherence to rules—skills that are essential for AI to navigate real-world scenarios. As AI models advance, they face growing challenges in interpreting and following rules, a critical step toward achieving artificial general intelligence.

The Gardner Test: A New Benchmark

To push the boundaries of AI development, a new evaluation known as the Gardner test has been proposed. This test requires an AI to understand and follow the rules of a game without assistance, with the rules disclosed at the start. The Gardner test builds on the principles of general game playing (GGP), a field pioneered by Michael Genesereth at Stanford University. In GGP competitions, AI systems compete in games with rules revealed at the beginning, testing their adaptability and strategic thinking.

The Gardner test advances this concept by introducing game rules expressed in natural language, a feat made possible by recent breakthroughs in large language models (LLMs) like ChatGPT, Claude, and Llama. This new evaluation aims to include games such as Connect Four, Hex, and Pentago, as well as those popularized by Gardner himself. The design of the test could involve collaboration between the GGP research community, AI developers, and enthusiasts of Gardner's work.

Adapting to New Rules: The Future of AI

To succeed in the Gardner test, AI systems must be capable of mastering any strategy game on the fly. Strategy games demand the ability to think ahead, handle unpredictable responses, adapt to changing objectives, and conform to strict rules. Current AI models, such as AlphaZero, rely on knowing the rules in advance to train their algorithms. While AlphaZero excels at mastering games through self-play, it cannot play a game it has not been trained on.

An AI that performs well on the Gardner test would demonstrate the ability to adapt to new rules, even without prior data. Such an AI could play any game and follow any novel rule set with precision, a hallmark of true artificial general intelligence. This level of adaptability is crucial for creating AI systems that can handle real-world complexities, where mistakes in rule-following could have catastrophic consequences.

For example, in national security, AI systems must accurately apply rules of engagement or negotiate subtle differences in legal and command authorities. In finance, programmable money requires AI to obey rules of ownership and transferability, where errors could lead to financial disasters. Building AI systems that can rigorously follow rules would enable the creation of more flexible and adaptable machine intelligences, capable of navigating uncertain and novel situations.

The Importance of Game Playing in AI Evolution

Game playing with a novel set of rules is essential for the next phase of AI evolution. It allows researchers to develop AI systems that are not only powerful but also safe. By testing AI's ability to play games on the fly, researchers can ensure that these systems meticulously follow the rules set for them, a critical step toward creating reliable and human-like AI.

In conclusion, the Gardner test represents a significant milestone in AI research. It challenges AI systems to adapt to new rules, pushing the boundaries of artificial general intelligence. By focusing on game playing, researchers can develop AI that is flexible, adaptable, and capable of handling the complexities of the real world.