  Commonsense reasoning simulates the human ability to make presumptions about
our physical world, and it is an essential cornerstone in building general AI
systems. We propose a new commonsense reasoning dataset based on human's
Interactive Fiction (IF) gameplay walkthroughs as human players demonstrate
plentiful and diverse commonsense reasoning. The new dataset provides a natural
mixture of various reasoning types and requires multi-hop reasoning. Moreover,
the IF game-based construction procedure requires much less human interventions
than previous ones. Different from existing benchmarks, our dataset focuses on
the assessment of functional commonsense knowledge rules rather than factual
knowledge. Hence, in order to achieve higher performance on our tasks, models
need to effectively utilize such functional knowledge to infer the outcomes of
actions, rather than relying solely on memorizing facts. Experiments show that
the introduced dataset is challenging to previous machine reading models as
well as the new large language models with a significant 20% performance gap
compared to human experts.
