The Game of Hidden Rules: A New Challenge for Machine Learning

Eric Pulick, Shubham Kumar Bharti, Yiding Chen, Vladimir Menkov, Yonatan Dov Mintz, Paul Kantor, Vicki M. Bier

Keywords: benchmark, environment, rule learning

TL;DR: We present a new learning environment allowing researchers to rigorously study how the characteristics of learning tasks affect difficulty.

Abstract: Systematic examination of learning tasks remains an important but understudied area of machine learning (ML) research. To date, most ML research has focused on measuring performance on new tasks or surpassing state of the art performance on existing tasks. These efforts are vital but do not explain why some tasks are more difficult than others. Understanding how task characteristics affect difficulty is critical to formalizing ML's strengths and limitations; a rigorous assessment of which types of tasks are well-suited to a specific algorithm and, conversely, which algorithms are well-suited to a specific task would mark an important step forward for the field. To assist researchers in this effort, we introduce a novel learning environment designed to study how task characteristics affect measured difficulty for the learner. This tool frames learning tasks as a ``board-clearing game,'' which we call the Game of Hidden Rules (GOHR). In each instance of the game, the researcher encodes a specific rule, unknown to the learner, that determines which moves are allowed at each state of the game. The learner must infer the rule through play. We detail the game's expressive rule syntax and show how it gives researchers granular control over learning tasks. We present sample rules, a sample ML algorithm, and methods to assess algorithm performance. Separately, we provide additional benchmark rules, a public leaderboard for performance on these rules, and documentation for installing and using the GOHR environment.

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