A Hybrid Knowledge-Based System Using Conditionals and ASP With Interactive ModellingEnvironment and Application to Warehouse Planning(CASPER – Conditionals and ASP for Expert Reasoning)

这个项目的总体目标是设计一个基于答案集编程(ASP)和具有排名语义的合理条件句(OCF-条件句)的混合知识表示框架，其中技术(默认)知识和合理的专家知识可以结合起来，以专业和认知充足的方式支持创造性的技术任务，如仓库规划。由于需要在许多地方涉及人类，物流是一个理想的(学术)应用领域，它在高度动态的环境中对特定领域的知识建模提出了多重挑战，这些领域知识也是典型的和与许多其他应用领域相关的。ASP和OCFs(序数条件函数)都以其良好的逻辑性质和适合于对人类知识和推理进行建模而闻名。此外，ASP是非单调和不确定推理最成功的实现之一。我们通过开发基于网络的推理算法来提高这个优雅的推理框架的效率，从而促进了OCF方法的发展。在一个框架中具有这两种方法允许在各自的另一个框架中适当地考虑一个框架的特征，从而产生协同效应，例如通过经由OCF条件条件评估ASP解决方案的可信性，或者将ASP语义作为OCF条件条件的基本逻辑。它们将共同提供一个富有表现力的正式框架，大大推进当今人工智能中知识表示方法的领域。此外，在建立知识库时考虑了人类推理的特点，提高了知识建模的认知充分性。为了确保专业人员的充分性，提供了一个互动的建模环境，以便专家可以参与建模过程的许多阶段。实现了一个演示系统，用于以整体的方式找到合适的物流布局和仓库配置，作为本项目中将要开发的交互概念的证明：使用户能够定制混合框架中的知识，使知识表示和推理过程的基础通过解释变得易懂，以信息的方式解决冲突，并根据专业标准控制解决方案的质量。我们的项目解决了知识表示中的几个基本研究问题，特别是提出了混合推理、可信的基于网络的推理和认知建模的创新方法。将其嵌入学术应用程序的综合知识表示任务中，也涉及新颖类型的解释和交互，确保稳健的一致性以及专业和认知上适当的建模，允许显示为推动可解释和可理解的人工智能用于现实世界应用而开发的方法和技术的相关性。

The overall goal of this project is to devise a hybrid knowledge representation framework based on answer set programming (ASP) and plausible conditionals with ranking semantics (OCF-conditionals) in which technical (default) knowledge and plausible expert knowledge can be combined to support creative technical tasks like warehouse planning in a professionally and cognitively adequate way. Due to the need for involving humans in many places, logistics is an ideal (academic) application domain that raises multiple challenges regarding the modelling of domain-specific knowledge in highly dynamic environments which are typical and relevant also for many other application areas. Both ASP and OCFs (ordinal conditional functions) are well-known for their good logical properties and their suitability to model human knowledge and reasoning. Moreover, ASP is one of the most successful implementations of nonmonotonic and uncertain reasoning. We advance the state of the art of the OCF methodology by developing network-based inference algorithms that improve the efficiency of this elegant reasoning framework. Having both approaches in one framework allows for taking characteristics of one framework into account suitably in the respective other so that synergies arise, e.g., by assessing the plausibility of ASP solutions via OCF-conditionals, or taking ASP semantics as a base logic for OCF-conditionals. Jointly they will provide an expressive formal framework that advances significantly the realm of methods of today‘s knowledge representation in Artificial Intelligence. Moreover, we take characteristics of human reasoning into account when setting up the knowledge bases to improve the cognitive adequacy of the knowledge modelling. To ensure professional adequacy, an interactive modelling environment is provided so that experts can be involved in many stages of the modelling process. A demonstrator system for finding suitable logistic layouts and configurations of warehouses in a holistic way is implemented as a proof of the interactive concepts to be developed in this project: enabling the user to customize knowledge in the hybrid framework, making basics of knowledge representation and reasoning processes intelligible by explanations, resolving conflicts in an informative way, and controlling the quality of solutions according to professional standards. Our project addresses several fundamental research issues in knowledge representation, proposing in particular innovative approaches to hybrid reasoning, plausible network-based inference, and cognitive modelling. Embedding this in a comprehensive knowledge representation task for an academic application, also involving novel types of explanations and interactions ensuring robust consistency and a professionally and cognitively adequate modelling, allows for showing the relevance of the methods and techniques to be developed for advancing explainable and intelligible AI for real-world applications.