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38
Chapter 4 PPT: A Probabilistic Approach to Extracting Preferential
Probabilities from Discussion Transcripts
This chapter presents an implicit probabilistic approach for extracting a
projection of aggregated design team preference information from design team
discussion as if the team is a single entity. It further takes into consideration how the
design preference information of a team can evolve over time as the team changes its
priorities based on new design information. Two initial models are given for
representing the most probable and preferred design alternative from the transcripts of
design team discussion, and for predicting how preferences might change from one
time interval to the next. This section examines three aspects of preferences in design
teams: design preference extraction, projecting an aggregation and understanding
preference evolution over the life of a project, and presents a case example to illustrate
its approaches. For the sake of compactness, the probabilistic approached in this
chapter is named with PPT (Preferential Probabilities from Transcripts).
Figure 1.1 shows that the preference data are acquired from the utterance data of
the transcript. But the linkage between them is not as easy as the other steps in Figure
1.1 because the models and the parameters of the models linking the utterances and
preferences are unknown. The basic procedures of this approach to resolve this
problem are shown as follows:
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 50 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 38 Chapter 4 PPT: A Probabilistic Approach to Extracting Preferential Probabilities from Discussion Transcripts This chapter presents an implicit probabilistic approach for extracting a projection of aggregated design team preference information from design team discussion as if the team is a single entity. It further takes into consideration how the design preference information of a team can evolve over time as the team changes its priorities based on new design information. Two initial models are given for representing the most probable and preferred design alternative from the transcripts of design team discussion, and for predicting how preferences might change from one time interval to the next. This section examines three aspects of preferences in design teams: design preference extraction, projecting an aggregation and understanding preference evolution over the life of a project, and presents a case example to illustrate its approaches. For the sake of compactness, the probabilistic approached in this chapter is named with PPT (Preferential Probabilities from Transcripts). Figure 1.1 shows that the preference data are acquired from the utterance data of the transcript. But the linkage between them is not as easy as the other steps in Figure 1.1 because the models and the parameters of the models linking the utterances and preferences are unknown. The basic procedures of this approach to resolve this problem are shown as follows: |
