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141
[12] Bierlaire, M., Discrete Choice Models, in Operations Research and Decision Aid
Methodologies in Traffic and Transportation Management, M. Labbe, et al.,
Editors. 1998, Springer Verlag. p. 203-227.
[13] Bilmes, J.A., A Gentle Tutorial of the EM Algorithm and its Application to
Parameter Estimation for Gaussian Mixture and Hidden Markov Models.
Technical Report. 1998, International Computer Science Institute: Berkeley,
CA, USA.
[14] Bockenholt, U., A Thurstonian Analysis of Preference Change. Journal of
Mathematical Psychology, 2002. 46(3): p. 300-314.
[15] Brans, J.P. and P. Vincke, A Preference Ranking Organisation Method: (The
PROMETHEE Method for Multiple Criteria Decision-Making). Management
Science, 1985. 31(6): p. 647-656.
[16] Breese, J.H., D. and C. Kadie. Empirical analysis of predictive algorithms for
collaborative filtering. in the Fourteenth Annual Conference on Uncertainty in
Artificial Intelligence. 1998: Morgan Kaufmann Publishers.
[17] Brockman, J.B. Evaluation of Student Design Processes. in The 26th Annual
Frontiers in Education Conference. 1996. Salt Lake City, Utah, USA.
[18] Burton, R.R., Semantic grammar: an engineering technique for constructing
natural language understanding systems. ACM SIGART Bulletin,
DEPARTMENT: Natural language interfaces, 1977(61): p. 26-26.
[19] Busemeyer, J.R. and A. Diederich, Survey of Decision Field Theory.
Mathematical Social Sciences, 2002. 43(3): p. 345-370.
[20] Canny, J. Collaborative filtering with privacy via factor analysis. in the 25th
annual international ACM SIGIR conference on Research and development in
information retrieval. 2002. Tampere, Finland: ACM Press.
[21] Chakrabarti, A. and T.P. Bligh, Approach to functional synthesis of mechanical
design concepts: theory, applications, and emerging research issues. Artificial
Intelligence for Engineering Design, Analysis and Manufacturing, 1996. 10(4):
p. 313-331.
[22] Chiu, I. and L.H. Shu. Bridging Cross-Domain Terminology for Biomimetic
Design. in ASME 2005 International Design Engineering Technical
Conferences & Computers and Information in Engineering Conference. 2005.
Long Beach, California, USA.
Object Description
| Title | Extraction of preferential probabilities from early stage engineering design team discussion |
| Author | Ji, Haifeng |
| Author email | haifengj@usc.edu; haifeng.ji@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Industrial & Systems Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-08-19 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-07 |
| Advisor (committee chair) | Yang, Maria C. |
| Advisor (committee member) |
Lu, Stephen Jin, Yan |
| Abstract | Activities in the early stage of engineering design typically include the generation of design choices and selection among these design choices. A key notion in design alternative selection is that of preference in which a designer or design team assigns priorities to a set of design choices. However, preferences become more challenging to assign on both a practical and theoretical level when done by a group of individuals. Preferences may also be explicitly obtained via surveys or questionnaires in which designers are asked to rank the choices, rate choice with values, or select a "most-preferred" choice. However, these methods are typically employed at a single point of time; therefore, it may not be practical to use surveys to elicit a team’s preference change and evolution throughout the process.; This research explores the text analysis on the design discussion transcripts and presents a probabilistic approach for implicitly extracting a projection of aggregated preference-related information from the transcripts. The approach in this research graphically represents how likely a choice is to be "most preferred" by a design team over time. For evaluation purpose, two approaches are established for approximating a team's "most preferred" choice in a probabilistic way from surveys of individual team members. A design selection experiment was conducted to determine possible correlations between the preferential probabilities estimated from the team's discussion and survey ratings explicitly stated by team members. Results suggest that there are strong correlations between extracted preferential probabilities and team intents that are stated explicitly, and that the proposed methods can provide a quantitative way to understand and represent qualitative design information using a low overhead information extraction method. |
| Keyword | preferences; probabilities; concept selection; design process; design decision-making |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1635 |
| Contributing entity | University of Southern California |
| Rights | Ji, Haifeng |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Ji-2413 |
| Archival file | uscthesesreloadpub_Volume14/etd-Ji-2413.pdf |
Description
| Title | Page 153 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 141 [12] Bierlaire, M., Discrete Choice Models, in Operations Research and Decision Aid Methodologies in Traffic and Transportation Management, M. Labbe, et al., Editors. 1998, Springer Verlag. p. 203-227. [13] Bilmes, J.A., A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Technical Report. 1998, International Computer Science Institute: Berkeley, CA, USA. [14] Bockenholt, U., A Thurstonian Analysis of Preference Change. Journal of Mathematical Psychology, 2002. 46(3): p. 300-314. [15] Brans, J.P. and P. Vincke, A Preference Ranking Organisation Method: (The PROMETHEE Method for Multiple Criteria Decision-Making). Management Science, 1985. 31(6): p. 647-656. [16] Breese, J.H., D. and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. in the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence. 1998: Morgan Kaufmann Publishers. [17] Brockman, J.B. Evaluation of Student Design Processes. in The 26th Annual Frontiers in Education Conference. 1996. Salt Lake City, Utah, USA. [18] Burton, R.R., Semantic grammar: an engineering technique for constructing natural language understanding systems. ACM SIGART Bulletin, DEPARTMENT: Natural language interfaces, 1977(61): p. 26-26. [19] Busemeyer, J.R. and A. Diederich, Survey of Decision Field Theory. Mathematical Social Sciences, 2002. 43(3): p. 345-370. [20] Canny, J. Collaborative filtering with privacy via factor analysis. in the 25th annual international ACM SIGIR conference on Research and development in information retrieval. 2002. Tampere, Finland: ACM Press. [21] Chakrabarti, A. and T.P. Bligh, Approach to functional synthesis of mechanical design concepts: theory, applications, and emerging research issues. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 1996. 10(4): p. 313-331. [22] Chiu, I. and L.H. Shu. Bridging Cross-Domain Terminology for Biomimetic Design. in ASME 2005 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference. 2005. Long Beach, California, USA. |
