Grounding Conceptual Knowledge with Spatio-Temporal Multi-Dimensional Relational Framework Trees

The real world is composed of sets of objects that have multidimensional properties and relations. Whether an agent is planning the next course of action in a task or making predictions about the future state of some object, useful taskoriented concepts are often encoded in terms of the complex interactions between the multi-dimensional attributes of subsets of these objects and of the relationships that exist between them. In this paper, we present extensions to the Spatiotemporal Multi-dimensional Relational Framework (SMRF) Trees, a data mining technique that extends the successful Spatiotemporal Relational Probability Tree models. From a set of labeled, multi-object examples of some target concept, our algorithm infers both the set of objects that participate in the concept, as well as the key object and relational attributes that characterize the concept. In contrast to other relational model approaches, SMRF trees do not require that categorical relations between objects to be defined a priori. Instead, our algorithm infers these categories from the continuous attributes of the objects and relations in the training data. In addition, our approach explicitly acknowledges the multi-dimensional nature of attributes, such as position, orientation and color in the creation of these categories. We present an updated learning algorithm for the SMRF approach, and validate our updated algorithm in both two and three dimensional domains that contain groups of static or moving objects.