Using Stories to Create Qualitative Representations of Motion

Juan Purcalla Arrufi , Alexandra Kirsch (1, 2, 3)

1 Institute for Advanced Studies
2 Intelligent Autonomous Systems
3 Human-Computer Interaction and Artificial Intelligence

Qualitative representations of motion transform kinematic floating point data into a finite set of concepts. Their main advantage is that they usually reflect a human understanding of the moving system, so we can more straightforwardly implement human-like navigation rules; in addition, they reduce the overhead of floating point computations. Therefore, they are an asset for mobile robots or unmanned vehicles— both terrestrial and aerial—especially those that interact with humans. In this paper we provide a method to create new qualitative representations of motion from any qualitative spatial representation by using a story-based approach.

Domaines: Intelligence artificielle [cs.AI]; Sciences cognitives

Juan Purcalla Arrufi, Alexandra Kirsch. Using Stories to Create Qualitative Representations of Motion. Problem-solving, Creativity and Spatial Reasoning Symposium (ProSocrates), 2017, Delmenhorst, Germany. ⟨hal-01698660⟩