An Ontological Hierarchy for Spatial Knowledge

空间知识的本体论层次结构空间知识是一种重要而普遍的常识性知识。它还提出了计算思维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示推理之间的关系。在这个项目中，一个被称为空间语义层次(SSH)的空间知识表示层次正在被形式化，以便它可以更广泛地使用。形式化将准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交互。它还将确定空间模型应用传感器、效应器和环境所需的最小前提条件，并确定从经验中学习分层空间模型的实例所需的机器学习和发现所需的最小目标集。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也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.