Matthew I. Campbell, Jonathan Cagan
Computational Design Laboratory
Department of Mechanical Engineering
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
AND
Kenneth Kotovsky
Department of Psychology
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
This paper presents an approach to managing the
complexities of automated design synthesis techniques. Often computational
approaches model design as a search process or as an optimization problem. The
nature of such techniques, however, does not allow the user to interact with the
search process once it has begun. Furthermore, traditional computational search
lacks the ability to learn from experience. While computational search
techniques have the ability to search many design alternatives quickly, the
human engineer can often arrive at a more elegant and robust solution by
applying heuristics learned from past experiences. The method introduced here
improves the capabilities of design synthesis methods by allowing for user input
and by making decisions based on previous experience. This method is
encapsulated within a software agent that is incorporated in an existing design
synthesis technique known as A-Design. In studying past candidate designs, the
process learns to be more effective in searching for solutions. Results show how
such a technique improves the quality of designs and efficiency of an existing
automated search process.