Interactive analogical retrieval: practice, theory and technology

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.PhDCommittee Chair: Goel, Ashok; Committee Member: Kolodner, Janet; Committee Member: Maher, Mary Lou; Committee Member: Nersessian, Nancy; Committee Member: Yen, Jeannett

Evaluating Biological Systems for Their Potential in Engineering Design

A team of biologists, engineers, and cognitive scientists has been working together for the past five years, teaching an upper level undergraduate course in biologically inspired design where half the class of forty students are biologists and other physical scientists and the other half are engineers (mechanical, materials, industrial, others). From this experience, we provide insights on how to teach students to evaluate biological systems for their potential in engineering design. We have found that at first, students are not familiar with developing their own question since, in most engineering design classes, the problem is prescribed along with clients who would like to have them solved. In our class, we challenge the students with defining a significant problem. The students with common challenges then are placed together in an interdisciplinary team with at least one biologist and one engineer. A detailed problem decomposition follows, identifying the hierarchy of systems and clearly specifying functions. This is essential for the next step of analogical reasoning. Analogical reasoning as applied to BID is a process of matching biological functions to engineered functions and transferring functions and mechanisms from biology to engineering. For each desired function, students may ask: what mechanisms does nature use for achieving the function? This question guides the exploration of the wealth of knowledge in biology by asking them to clearly define the function of interest, then search for natural processes that perform this function. To expand on this search space, the students next make a list of the same function performed by other organisms for a comparative analysis to deepen their understanding and extract key biological principles. Students then invert the function and identify keywords to search. They also must refer to general biology books to identify key organisms that perform the function the best (and hence are included in textbooks). Using databases, such as the Web of Science functions, they can try to select the ‘best’ articles. If one is lucky, a single biological system may serve as a near perfect match to lead to a successful BID. However, some of the most innovative designs are built from more than one biological system, something that evolution cannot always do. We call these compound analogies. At this point, the design iteration can take on a different approach, namely solution based rather than problem based. Here, the team takes a natural system and asks, how can this biological principle improve an engineered design or function. These twin processes: solution vs problem-based approaches both have led to innovative and creative design concepts in this interdisciplinary class. Key words: Biological systems; engineering design; interdisciplinary clas

Goal Reasoning: Papers from the ACS Workshop

This technical report contains the 14 accepted papers presented at the Workshop on Goal Reasoning, which was held as part of the 2015 Conference on Advances in Cognitive Systems (ACS-15) in Atlanta, Georgia on 28 May 2015. This is the fourth in a series of workshops related to this topic, the first of which was the AAAI-10 Workshop on Goal-Directed Autonomy; the second was the Self-Motivated Agents (SeMoA) Workshop, held at Lehigh University in November 2012; and the third was the Goal Reasoning Workshop at ACS-13 in Baltimore, Maryland in December 2013

Mediated Analogy: From Practice to Theory to Collaborative Technology

Biologically inspired design espouses the adaptation of functions and mechanisms in biological systems to solve engineering problems in innovative ways. Analogy occupies a central place in biologically inspired design cognition. One aspect of analogy that has come to the fore through our in situ studies of biologically inspired design is the notion of mediated analogy, where the process of obtaining source analogues relevant to the target is significantly mediated by the external environment consisting of people, tools and media. A theory of mediated analogy is developed here which focuses on how analogists use external information environments, or mediational means, when engaging in analogical problem solving. This theory is then used as a basis for analyzing the affordances or lack thereof of common online information environments (e.g., Google Scholar, Web of Science) used by biologically inspired designers for supporting mediated analogy. Two general principles that can be applied to enhance the affordances of such environments to better support mediated analogy are also derived from this theory: the principle of proximal cues systematicity and the patch consumption principle. These two principles are applied to develop Biologue, a novel online collaborative knowledge-sharing environment that is designed to better: (1) facilitate the exchange of scholarly biology articles between biologists and engineers, and (2) promote conceptual understanding of biological systems discussed in those articles among engineers, both in the service of aiding the practice of biologically inspired design

Compound Analogical Design, Or How to Make a Surfboard Disappear

Biologically-inspired design uses analogous biological systems to develop novel solutions for human needs. In this paper we describe an in situ cognitive study of biologically inspired engineering design. We found that biologically inspired engineering design often involves compound analogies in which a new design concept is generated by composing the results of multiple cross-domain analogies. This process of compound analogy relies on an opportunistic interaction between two processes: problem decomposition and analogical transfer. Based on this cognitive study, we also present an information-processing account of compound analogies

Comparison of Methods for Using Reduced Models to Speed Up Design Optimization

In this paper we compare two methods for forming reduced models to speed up genetic-algorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization. Empirical results in several engineering design domains are presented

Comparison of Methods for Using Reduced Models to Speed Up

In this paper we compare two methods for forming reduced models to speed up geneticalgorithm -based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization. One method speeds up the optimization by making the genetic operators more informed. The other method speeds up the optimization by genetically engineering some individuals instead of using the regular Darwinian evolution approach

Comparison of methods for developing dynamic reduced models for design optimization

Abstract- In this paper we compare three methods for forming reduced models to speed up geneticalgorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed. Empirical results in several engineering design domains are presented. I