A Representation Scheme for Description and Reconstruction of Object Configurations Based on Qualitative Relations
Linköping University, Department of Computer and Information Science, CASL - Cognitive Autonomous Systems Laboratory
Linköping University, The Institute of Technology
Doctoral thesis, monograph (Other academic)
Place of publ.:
Linköping University Electronic Press
Institutionen för datavetenskap
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524; 1204
Qualitative Reasoning, Spatial Reasoning, Cognitive Modeling, Human-Machine Interaction, Coarse Reasoning, Artificial Intelligence
One reason Qualitative Spatial Reasoning (QSR) is becoming increasingly important to Artificial Intelligence (AI) is the need for a smooth ‘human-like’ communication between autonomous agents and people. The selected, yet general, task motivating the work presented here is the scenario of an object configuration that has to be described by an observer on the ground using only relational object positions. The description provided should enable a second agent to create a map-like picture of the described configuration in order to recognize the configuration on a representation from the survey perspective, for instance on a geographic map or in the landscape itself while observing it from an aerial vehicle. Either agent might be an autonomous system or a person. Therefore, the particular focus of this work lies on the necessity to develop description and reconstruction methods that are cognitively easy to apply for a person.
This thesis presents the representation scheme QuaDRO (Qualitative Description and Reconstruction of Object configurations). Its main contributions are a specification and qualitative classification of information available from different local viewpoints into nine qualitative equivalence classes. This classification allows the preservation of information needed for reconstruction nto a global frame of reference. The reconstruction takes place in an underlying qualitative grid with adjustable granularity. A novel approach for representing objects of eight different orientations by two different frames of reference is used. A substantial contribution to alleviate the reconstruction process is that new objects can be inserted anywhere within the reconstruction without the need for backtracking or rereconstructing. In addition, an approach to reconstruct configurations from underspecified descriptions using conceptual neighbourhood-based reasoning and coarse object relations is presented.
2008-09-29, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Doctor of Philosophy (PhD)
Sandewall, Erik, Professor (Linköping University, Department of Computer and Information Science, CASL - Cognitive Autonomous Systems Laboratory) (Linköping University, The Institute of Technology)
Klippel, Alexander, Professor (The Pennsylvania State University, USA)