The research and development of a simulation-based
decision support system (SB-DSS) capable of assisting early
collaborative design processes is presented. The requirements
for such a system are included. Existing collaborative
DSSs are shown to lack the capability to manipulate complex
simulation-based relationships. On the other hand, advances
within the machine learning in design community are shown
to have a potential for providing, but have not yet addressed,
simulation-based support for collaborative design processes.
The developed SB-DSS is described in terms of its four
principal components. First, the behavior-evaluation (BE)
model is used to both structure individual, domain-specific
decision models and organize these models into a collaborative
decision model. Second, a probabilistic framework for the
BE model enables management of the uncertainty inherent
in learning and using simulation-based knowledge. Significantly,
this framework provides a constraint satisfaction environment
in which simulation-based knowledge is used. Third, a statistical
neural network approach is used to capture simulation-based
knowledge and build the probabilistic behavior models based
on this knowledge. Fourth, since a probability distribution
theory does not exist for the nonlinear neural network
approaches, Monte Carlo simulation is introduced as a method
to sample the trained neural networks and approximate the
likelihoods of design variable values. Consequently, constraint
satisfaction problem-solving capability is obtained. In
addition, a mapping of the SB-DSS architecture onto a collaborative
design agent framework is provided. Experimental evaluation
of a prototype SB-DSS system is summarized, and performance
of the SB-DSS with respect to search and usability metrics
is documented. Initial results in developing the simulation-based
support for collaborative design are encouraging. Lastly,
a categorization of the machine learning approach and a
critique of the proposed categorization scheme is presented.