Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts, symbolic knowledge injection (SKI) proposes algorithms aimed at steering sub-symbolic predictors’ learning towards compliance w.r.t. pre-existing symbolic knowledge bases. However, state-of-the-art contributions about SKI mostly tackle injection from a foundational perspective, often focussing solely on improving the predictive performance of the sub-symbolic predictors undergoing injection. Technical contributions, in turn, are tailored on individual methods/experiments and therefore poorly interoperable with agent technologies as well as among each others. Intelligent agents may exploit SKI to serve many purposes other than predictive performance alone—provided that, of course, adequate technological support exists: for instance, SKI may allow agents to tune computational, energetic, or data requirements of sub-symbolic predictors. Given that different algorithms may exist to serve all those many purposes, some criteria for algorithm selection as well as a suitable technology should be available to let agents dynamically select and exploit the most suitable algorithm for the problem at hand. Along this line, in this work we design a set of quality-of-service (QoS) metrics for SKI, and a general-purpose software API to enable their application to various SKI algorithms—namely, platform for symbolic knowledge injection (PSyKI). We provide an abstract formulation of four QoS metrics for SKI, and describe the design of PSyKI according to a software engineering perspective. Then we discuss how our QoS metrics are supported by PSyKI. Finally, we demonstrate the effectiveness of both our QoS metrics and PSyKI via a number of experiments, where SKI is both applied and assessed via our proposed API. Our empirical analysis demonstrates both the soundness of our proposed metrics and the versatility of PSyKI as the first software tool supporting the application, interchange, and numerical assessment of SKI techniques. To the best of our knowledge, our proposals represent the first attempt to introduce QoS metrics for SKI, and the software tools enabling their practical exploitation for both human and computational agents. In particular, our contributions could be exploited to automate and/or compare the manifold SKI algorithms from the state of the art. Hence moving a concrete step forward the engineering of efficient, robust, and trustworthy software applications that integrate symbolic agents and sub-symbolic predictors.

连接智能符号代理和子符号预测器是人工智能领域的一个长期研究目标。在最近的集成工作中，符号知识注入（SKI）提出了旨在引导子符号预测器的学习符合预先存在的符号知识库的算法。然而，关于 SKI 的最先进的贡献大多从基础角度解决注入问题，通常只专注于提高正在注入的子符号预测器的预测性能。反过来，技术贡献是根据单独的方法/实验定制的，因此与代理技术以及彼此之间的互操作性很差。智能代理可以利用 SKI 来实现除了预测性能之外的许多目的——当然，前提是存在足够的技术支持：例如，SKI 可以允许代理调整子符号预测器的计算、能量或数据要求。鉴于可能存在不同的算法来满足所有这些多种目的，因此应该提供一些算法选择标准以及合适的技术，以便让代理动态选择和利用最适合当前问题的算法。沿着这条线，在这项工作中，我们为 SKI 设计了一套服务质量(QoS)指标，以及一个通用软件 API，以使其能够应用于各种 SKI 算法，即符号知识注入平台 (PSyKI) 。我们为 SKI 提供了四个 QoS 指标的抽象表述，并根据软件工程的角度描述了 PSyKI 的设计。然后我们讨论 PSyKI 如何支持我们的 QoS 指标。最后，我们通过大量实验证明了 QoS 指标和 PSyKI 的有效性，其中 SKI 通过我们提出的 API 进行应用和评估。我们的实证分析证明了我们提出的指标的合理性以及 PSyKI 作为第一个支持 SKI 技术的应用、交换和数值评估的软件工具的多功能性。据我们所知，我们的提案代表了为 SKI 引入 QoS 指标的首次尝试，以及使人类和计算代理能够实际利用它们的软件工具。特别是，我们的贡献可以用于自动化和/或比较现有技术中的多种 SKI 算法。因此，在集成符号代理和子符号预测器的高效、稳健和值得信赖的软件应用程序的设计方面迈出了具体的一步。