发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
探讨逻辑与优化结合对基于规则的人工智能的贡献，综述二者合作的多个领域，介绍利用决策图等计算投影、用最优性后分析增强透明度及优化在相关编程中的作用，并指出未来研究方向。
AI 中文摘要
逻辑和优化相结合能为基于规则的人工智能做出重要贡献。逻辑用于编码规则库并从中推理，优化为计算推理提供强大技术。在人工智能透明度受关注的当下，它们的结合有了新意义。本文综述了逻辑 - 优化合作的几个领域，展示了如何用决策图和基于逻辑的Benders分解计算投影，描述了用最优性后分析增强透明度及优化在模理论回答集编程中的作用，并给出未来研究方向。
英文摘要
Logic and optimization can, in combination, make valuable contributions to rule-based AI. Logic is the obvious medium for encoding a rule base and drawing inferences from it, while optimization provides a powerful technology for computing inferences. Their combination has taken on new relevance amid a growing concern for transparency in AI. which is important for reproducibility, explainability, trustworthiness, and fairness. Rule-based AI provides a natural solution to transparency that is becoming increasingly practical due to today's highly advanced optimization methods. This article surveys several areas of logic-optimization partnership, including probabilistic logic, Bayesian logic, belief logics and Dempster-Shafer theory, nonmonotonic (default) logic, many-valued logics, and inference of logical formulas from noisy data based on Boolean regression. It shows how to compute projections, the fundamental problem of both logic and optimization, using decision diagrams and logic-based Benders decomposition. It describes the use of postoptimality analysis to explain how conclusions are reached, further enhancing transparency, as well as the role of optimization in answer set programming modulo theories. The paper concludes by suggesting possible future research directions.
