Recently, counterfactuals using "if-only" explanations have become very
popular in eXplainable AI (XAI), as they describe which changes to
feature-inputs of a black-box AI system result in changes to a (usually
negative) decision-outcome. Even more recently, semi-factuals using "even-if"
explanations have gained more attention. They elucidate the feature-input
changes that do \textit{not} change the decision-outcome of the AI system, with
a potential to suggest more beneficial recourses. Some semi-factual methods use
counterfactuals to the query-instance to guide semi-factual production
(so-called counterfactual-guided methods), whereas others do not (so-called
counterfactual-free methods). In this work, we perform comprehensive tests of 8
semi-factual methods on 7 datasets using 5 key metrics, to determine whether
counterfactual guidance is necessary to find the best semi-factuals. The
results of these tests suggests not, but rather that computing other aspects of
the decision space lead to better semi-factual XAI.

通过在 7 个数据集上使用 5 个关键指标对 8 种半事实方法进行全面测试，确定是否需要使用反事实指导来找到最佳的半事实解释，结果表明不需要，而是计算决策空间的其他方面能够得到更好的半事实可解释性。

即便是从仅有的条件中获得的：以反事实为指导的最佳半事实性解释能否找到？

Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found  Using Counterfactuals As Guides?

Metaheuristics are gradient-free and problem-independent search methods. They
have gained huge success in solving various optimization problems in academia
and industry. Automated metaheuristic algorithm design is a promising
alternative to human-made design. This paper proposes a methodological
framework, AutoOpt, for automatically designing metaheuristic algorithms for
optimization problems. AutoOpt consists of: (1) a bi-level criterion to
evaluate the designed algorithms' performance; (2) a general schema of the
decision space from where the algorithms will be designed; (3) a mixed graph-
and real number-based representation to represent the designed algorithms; and
(4) a model-free method to conduct the design process. AutoOpt benefits
academic researchers and practical users struggling to design metaheuristic
algorithms for optimization problems. A real-world case study demonstrates
AutoOpt's effectiveness and efficiency.

本研究针对优化问题，提出了一种基于自动设计元启发式算法的方法学框架 AutoOpt，该框架包含具有良好性能的算法性能评估标准、算法设计决策空间的一般架构、设计算法所用的混合图和实数表示以及无模型方法来进行设计过程。实测结果表明，AutoOpt 是一种有效和高效的设计元启发式算法的方法学框架。