An Ontological Hierarchy for Spatial Knowledge

空间知识的本体论层次空间知识是一种重要且普遍存在的常识性知识。它也提出了心智计算模型面临的一个关键问题：连续世界中的行为与离散符号表征的推理之间的关系。在这个项目中，空间知识表示的层次结构被称为空间语义层次（SSH），该层次结构是在之前的NSF资助中开发的，因此它可以更广泛地使用。形式化将精确地阐明不同的描述性本体和推理方法何时以及如何有效地相互作用。它还将确定应用空间模型所需的传感器、效应器和环境的最小前提条件，并确定从经验中学习分层空间模型实例所需的机器学习和发现的最小目标集。它将在模拟和物理机器人上进行数学和实验评估。研究结果对人工智能的基础具有重要的理论意义，对智能机器人的应用具有重要的实践意义。

IRI-9504138 Kuipers, Benjamin University of Texas-Austin $93,501-12 mos An Ontological Hierarchy for Spatial Knowledge Spatial knowledge is an important and ubiquitous kind of commonsense knowledge. it also raises a critical issue facing computational models of mind: the relationship between behavior in the continuous world and inference with discrete symbolic representations. in this project, a hierarchy of representations for spatial knowledge called the Spatial Semantic Hierarchy (SSH) developed in a previous NSF grant is being formalized so that it can be more widely useful. The formalization will clarify precisely when and how different descriptive ontologies and inference methods can interact effectively. It will also identify the minimal preconditions on sensors, effectors, and environment required for the spatial model to apply and identify the minimal set of targets for machine learning and discovery required to learn an instance of the hierarchical spatial model from experience. It will be evaluated both mathematically and experimentally on simulated and physical robots. The results should be theoretically important to the foundations of artificial intelligence and practically important to intelligent robotics applications.