The Compilation of
Design Decomposition Knowledge

Jingwen Liu

Advisor: David C. Brown

Ph.D. Thesis, completed August 1993

The goal of this research was to decompose design problems using many types of knowledge, such as knowledge of objects, functions, design cases, design heuristics, general problem-solving, the domain, and the requirements. We have proposed a static knowledge compilation mechanism which can generate good decompositions for parametric design problems. Knowledge Compilation is learning in which existing knowledge is converted into new forms, to improve problem-solving efficiency. The proposed mechanism uses a variety of types of available knowledge to synthesize decompositions for different design problems. We claim that the decompositions generated can reduce search and make design more efficient.

References:

J.Liu & D.C.Brown (June 1993) Compiling Design Decompositions from Other Knowledge. Proc. KCSL'93: Third International Workshop on Knowledge Compilation and Speedup Learning, Tenth International Conference on Machine Learning (ML93), UMass, Amherst, MA, pp. 107-111.

D.C.Brown & J.Liu (January 1994) Decomposition of Parametric Design Problems. Proc. 1994 NSF Design and Manufacturing Grantees Conference, MIT, Cambridge, MA, p.19

J.Liu & D.C.Brown (August 1994) Generating Design Decomposition Knowledge for Parametric Design Problems. Artificial Intelligence in Design '94, (Eds.) Gero & Sudweeks, Kluwer Academic Publishers, 1994, pp. 661-678.