| Title | Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively |
| Publication Type | Conference Proceedings |
| Year of Conference | 2019 |
| Authors | Booth, S., C. Muise, and J. Shah |
| Conference Name | International Joint Conference on Artificial Intelligence (IJCAI) |
| Date Published | 08/2019 |
| Conference Location | Macau, China |
| Keywords | explainability, explainable AI, interpretability, knowledge compilation, logic, propositional theories |
| 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. |
