(Georgia Institute of Technology, 2009)
Goel, Ashok K.; Adams, Summer; Cutshaw, Neil; Sugandh, Neha
The sensemaking task in investigative analysis generates models that connect entities and
events in an input stream of data. We describe two knowledge systems for aiding sensemaking in
investigative analysis. The Spade system uses crime schemas to generate an explanatory hypothesis
and past cases to validate the hypothesis. The STAB system represents crime schemas as
hierarchical scripts with goals and states. It generates multiple explanatory hypotheses for an input
data stream containing interleaved sequences of events, recognizes intent in a specific event
sequence, and calculates confidence values for the generated hypotheses. We view STAB and Spade
as automated cognitive assistants to human analysts: they may support sensemaking in investigative
analysis by generating and managing multiple competing hypotheses.
(Georgia Institute of Technology, 2007)
Vattam, Swaroop; Helms, Michael E.; Goel, Ashok K.
Biologically-inspired design uses analogous biological phenomena to develop solutions for engineering problems. Understanding, learning and practicing this approach to design is challenging because biologists and engineers speak different languages, have different perspectives on design, with different constraints on design problems and different resources for realizing an abstract design. In Fall 2006, we attended ME/ISyE/MSE/PTFe/BIOL 4803: Biologically-Inspired Design, an interdisciplinary introductory course for juniors and seniors offered at Georgia Tech. We collected course materials, took class notes, observed teacher-student and student-student interactions in the classroom. We also observed some sessions of a few interdisciplinary teams of students engaged in their design projects outside the classroom. We then analyzed the observations in terms of existing cognitive theories of design, modeling, and analogy. The goals of this cognitive study were to (1) understand the cognitive basis of biologically-inspired innovation in engineering design, (2) identify opportunities for enabling more effective learning of biologically-inspired design, and (3) examine the implications for developing computational tools for facilitating effective biologically-inspired design. This report summarizes our main observations about learning biologically-inspired design, and presents our preliminary analysis of biologically-inspired design in a classroom setting.