In research of manufacturing systems and autonomous robots, the term capability is used for a machine-interpretable specification of a system function. Approaches in this research area develop information models that capture all information relevant to interpret the requirements, effects and behavior of functions. These approaches are intended to overcome the heterogeneity resulting from the various types of processes and from the large number of different vendors. However, these models and associated methods do not offer solutions for automated process planning, i.e. finding a sequence of individual capabilities required to manufacture a certain product or to accomplish a mission using autonomous robots. Instead, this is a typical task for AI planning approaches, which unfortunately require a high effort to create the respective planning problem descriptions. In this paper, we present an approach that combines these two topics: Starting from a semantic capability model, an AI planning problem is automatically generated. The planning problem is encoded using Satisfiability Modulo Theories and uses an existing solver to find valid capability sequences including required parameter values. The approach also offers possibilities to integrate existing human expertise and to provide explanations for human operators in order to help understand planning decisions.

翻译：在制造系统与自主机器人的研究中，术语"能力"用于指代系统功能的机器可解释规范。该研究领域的方法致力于开发信息模型，以捕获解释功能需求、效果与行为相关的全部信息。这些方法旨在克服由多种工艺类型和大量不同供应商导致的异构性问题。然而，这些模型及其相关方法并未提供自动化工艺规划的解决方案——即寻找制造特定产品或使用自主机器人完成任务所需的单个能力序列。这恰恰是人工智能规划方法的典型任务，但遗憾的是，这类方法需要耗费大量精力来创建相应的规划问题描述。本文提出了一种融合上述两个主题的方法：从语义能力模型出发，自动生成人工智能规划问题。该规划问题采用可满足性模理论进行编码，并利用现有求解器寻找包含所需参数值的有效能力序列。该方法还支持整合现有的人类专业知识，并能向操作人员提供解释以帮助理解规划决策。