Optimal Recursive Designers´ Profile Estimation in Collaborative Declarative Environment

In general a design process is complex and requires the collaboration of several designers on the same product to improve its reliability, performance and efficiency. Though the increase of the Internet as a communication means that supports the sharing and transferring of knowledge, the collaborative declarative design phase lacks for a) imprecision in declaring the statements (ambiguity) and b) subjective interpretation of a scene with respect to the current designer´s profile. For this reason, on-line learning strategies should be applied, which models the actual user´s preferences. In this paper, we propose an efficient and adaptable learning strategy for dynamic modeling of a designer profile based an adaptable neural network architecture. The scheme optimally updates the network weights in a way that the current designers´ preferences are trusted as much as possible, while simultaneously a minimal degradation of the already obtained network knowledge is minimized. The algorithm requires low computational complexity and guarantees stable performance instead of conventional neural network training schemes, whose the solution is often trapped to local minima.