We are studying simplification by Analogy with stored simplifications. The designs are represented as Function, plus Behavior, plus Structure. Simplifications may occur at each of these levels, with consequences for the other levels of the representation. Experiments are being made with a prototype system that can simpify designs by analogy. SiFas provide elementary functionality specific to design tasks.
Agents can either learn or be learned about.
The specific learning models are intended to be evaluated in the context of the Single Function Agent paradigm, with a particular emphasis on parametric design problems. Capturing the design rationale, the alternatives considered while designing and the reasons for accepting or rejecting them, offers a richer view into both the product and the decision making process. This information could be invaluable as an aid in revising, maintaining, documenting, evaluating, and learning the design. Most research on design rationale has focussed on its capture and representation. This research takes a different approach by focussing on how the rationale could be used.