This project will further develop case-based reasoning (CBR) techniques for the automatic generation of multi-modal drawing-structure-behavior-function (DSBF) teleological models from line drawings. The focus in this project is on conceptual-that is, preliminary, qualitative-designs involving linear and rotational motion. The employed approach takes its motivations from current practice in computer-aided design (CAD), which uses a variety of "modal" information (e.g., sketches, drawings, 2-D and 3-D geometric models), and focuses on perceptual information. A long-term practical goal of the project is to contribute to a new generation of CAD tools that can support the use of teleological models in conjunction with drawings. The long-term theoretical goal is to build a computational theory of multi-modal CBR in teleological modeling and design. In this project, case memory consists of DSBF models of known drawings, and cases are indexed by their drawings as well as their functions. Given a target drawing of a kinematics device, the CBR system retrieves a DSBF model of a known drawing that is considered to be similar to the new drawing. The DSBF model then guides the transfer and adaptation of the DSBF model of the retrieved drawing into a candidate DSFB model for the new drawing. The proposed research is developing the idea of multi-modal patterns of regularity, called Generic Visual Teleological Mechanisms (GVTM). GVTM would be used to help map differences between the new and the known drawings into the DSBF model of the known drawing, and to enable the system to reason about the differences between drawings (e.g., different components, different configurations of the same components). The quality of the DSBF models constructed is to be evaluated by reusing them in a legacy system for a different ask, namely, automated generation of a structure for a design achieving a specified function. One of the broader impacts of this work is on the next generation of CAD systems and CBR systems in general. Another contribution would be the use of the SBF methodology for teleological modeling as a technique for teaching and learning causal models in science and engineering, for instance, by the research team at Georgia Tech working together on an NSF SLC (Science of Learning Centers) Catalyst grant.
