interpretable for applications involving temporal dependencies. [6/6 of https://arxiv.org/abs/2503.01792v1]
do so by infusing automata-theoretic techniques for LTLp inside a genetic algorithm for counterfactual generation. The empirical evaluation shows that the generated counterfactuals are temporally meaningful and more [5/6 of https://arxiv.org/abs/2503.01…
this challenge by introducing a novel approach for generating temporally constrained counterfactuals, guaranteed to comply by design with background knowledge expressed in Linear Temporal Logic on process traces (LTLp). We [4/6 of https://arxiv.org/abs/…
background knowledge expressing which dynamics are possible and which not. Specifically, counterfactuals generated off-the-shelf may violate the background knowledge, leading to inconsistent explanations. This work tackles [3/6 of https://arxiv.org/abs/…