An intelligent-agent framework for concurrent product design and planning

Tan, Gek Woo; Hayes, Caroline C.; Shaw, Michael J.

doi:10.1109/17.511840

Cited by 67 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Work group diversity shapes expectations of how others in the work group will behave (McGrath, Berdahl, & Arrow, 1995). In information-diverse groups, members have different experiences and work-relevant knowledge (Webber & Donahue, 2001), so they will expect that “other individuals may have significantly different experiences and perspectives on key issues and problems” (Milliken & Martins, 1996, p. 404), and will be encouraged to communicate actively with one another (e.g., Tan, Hayes, & Shaw, 1996). Moreover, the information-diverse group context, which implies varying experiences, modes of perception, and approaches to problems (Stahl, Mäkelä, Zander, & Maznevski, 2010), may cause members to doubt whether they possess sufficient expertise.…”

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

Diverse Work Groups and Employee Performance

Zhang

Huai

2015

Small Group Research

View full text Add to dashboard Cite

This study is a report of an investigation of group diversity—particularly informational diversity and social diversity—revealing its effects on individual performance through communication ties. Two studies, one using 127 groups of employees from six firms and another using 104 groups of part-time students from a training program, demonstrate that informational diversity affects individual task and creative performance through communication ties, whereas social diversity does not. Future research and practical implications are discussed.

show abstract

Section: Theoretical Background and Hypothesesmentioning

confidence: 99%

Diverse Work Groups and Employee Performance

Zhang

Huai

2015

Small Group Research

View full text Add to dashboard Cite

show abstract

“…Each agent had 'control' over a single design variable. The agents were able to observe each other through a blackboard approach [11]. The design task provided a global goal for the MAS.…”

Section: Uci Car Design Databasementioning

confidence: 99%

Comparing Stochastic Design Decision Belief Models: Pointwise versus Interval Probabilities

Matthews

2011

Design Computing and Cognition ’10

View full text Add to dashboard Cite

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. The underlying supporting belief model has traditionally been a probability distribution function (PDF) which uses pointwise probabilities for all possible outcomes. This can present a challenge during the knowledge elicitation process. To overcome this, it is proposed to test the performance of a credal set belief model. Credal sets (sometimes also referred to as p-boxes) use interval probabilities rather than pointwise probabilities and therefore are more easier to elicit from domain experts. The PDF and credal set belief models are compared using a design domain MAS which is able to learn, and thereby refine, the belief model based on its experience. The outcome of the experiment illustrates that there is no significant difference between the PDF based and credal set based belief models in the performance of the MAS.

show abstract

Agent Technology in Modern Manufacturing

2017

Multi‐Agent‐Based Production Planning and Control

View full text Add to dashboard Cite

An intelligent-agent framework for concurrent product design and planning

Cited by 67 publications

References 13 publications

Diverse Work Groups and Employee Performance

Diverse Work Groups and Employee Performance

Comparing Stochastic Design Decision Belief Models: Pointwise versus Interval Probabilities

Agent Technology in Modern Manufacturing

Contact Info

Product

Resources

About