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As machine learning (ML) is more tightly woven into society, it is imperativethat we better characterize ML's strengths and limitations if we are to employit responsibly. Existing benchmark environments for ML, such as board and videogames, offer well-defined benchmarks for progress, but constituent tasks areoften complex, and it is frequently unclear how task characteristics contributeto overall difficulty for the machine learner. Likewise, without a systematicassessment of how task characteristics influence difficulty, it is challengingto draw meaningful connections between performance in different benchmarkenvironments. We introduce a novel benchmark environment that offers anenormous range of ML challenges and enables precise examination of how taskelements influence practical difficulty. The tool frames learning tasks as a"board-clearing game," which we call the Game of Hidden Rules (GOHR). Theenvironment comprises an expressive rule language and a captive serverenvironment that can be installed locally. We propose a set of benchmarkrule-learning tasks and plan to support a performance leader-board forresearchers interested in attempting to learn our rules. GOHR complementsexisting environments by allowing fine, controlled modifications to tasks,enabling experimenters to better understand how each facet of a given learningtask contributes to its practical difficulty for an arbitrary ML algorithm.

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