Evaluating the Interpretability of the Knowledge Compilation Map:
Communicating Logical Statements Effectively
Abstract
Knowledge compilation techniques translate propositional theories into equivalent forms to increase their
computational tractability. But, how should we best
present these propositional theories to a human? We
analyze the standard taxonomy of propositional theories for relative interpretability across three model
domains: highway driving, emergency triage, and
the chopsticks game. We generate decision-making
agents which produce logical explanations for their
actions and apply knowledge compilation to these
explanations. Then, we evaluate how quickly, accurately, and confidently users comprehend the generated explanations. We find that domain, formula size,
and negated logical connectives significantly affect
comprehension while formula properties typically
associated with interpretability are not strong predictors of human ability to comprehend the theory