Classification: a basis for understanding tools in declarative modelling
Computer Networks and ISDN Systems - Special issue on graphics research and education on the World Wide Web
Intelligent profiling by example
Proceedings of the 6th international conference on Intelligent user interfaces
User Modeling and User-Adapted Interaction
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A machine learning approach for the support of preliminary structural design
Advanced Engineering Informatics
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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MultiCAD is a design environment that generates geometric models of buildings based on abstract declarative descriptions. The increased number of solutions produced has called for an intelligent module selecting those closer to user's preferences. We have proposed and implemented such a module, featuring two components: a Decision Support Component, capturing user preferences based on attribute weight assignment techniques (used in SMART, AHP and via RR), and a Machine Learning Component, learning preferences by incrementally training a neural network committee based on user evaluated solutions. Alternative configurations must be compared before actual use of the ML Component takes place. Due to the practical limitation on the number of solutions that can be inspected and evaluated by human users, an automated mechanism plays the role of a group of virtual users. The best performing configuration, regarding virtual users' preferences, will be integrated to the system and evaluated against actual human evaluation results.