Automated Reuse of Design Plans in Bogart

Mostow, Jack; Barley, Michael; Weinrich, Timothy

doi:10.1016/b978-0-12-660562-4.50009-1

Cited by 14 publications

(4 citation statements)

References 11 publications

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“…Having acquired the history of design decisions made in a previous design, BOGART (Mostow, 1989;Mostow et al, 1992) uses the derivational analogy method by Carbonell (1983Carbonell ( , 1986 to change the design plan by reasoning from the previous plan. The derivational analogy method represents a problem-solving plan as a hierarchical goal structure, showing how and why each goal was decomposed into subgoals.…”

Section: Similarity/dissimilarity Comparisonmentioning

confidence: 99%

A foundation for machine learning in design

Sim

Duffy

1998

AIEDAM

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This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, "What types of knowledge can be learnt?", "How does learning occur?", and "When does learning occur?". Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD

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Section: Similarity/dissimilarity Comparisonmentioning

confidence: 99%

A foundation for machine learning in design

Sim

Duffy

1998

AIEDAM

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“…Current GA-based machine learning systems (classifier systems) use rules to store past experience to improve their performance over time [1][2][3][4][5]. However, many application areas, especially in the design domain, are more suited to a case-based storage of past experience [6][7][8][9]. This paper proposes and describes a system that uses a case-base as a long-term knowledge store in a new GA-based design system that learns from experience.…”

Section: Introductionmentioning

confidence: 99%

Case injected genetic algorithms for learning across problems

Louis¹

2004

Engineering Optimization

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Genetic algorithms (GAs) augmented with a case-based memory of past design problem-solving attempts are used to obtain better performance over time on sets of similar design problems. Rather than starting anew on each design, a GA's population is periodically injected with appropriate intermediate design solutions to similar, previously solved design problems. Experimental results on configuration design problems: the design of parity checker and adder circuits, demonstrate the performance gains from the approach and show that the system learns to take less time to provide quality solutions to a new design problem as it gains experience from solving other similar design problems.

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“…Research in the field has been performed for several years and has led to a number of results. For example, CYCLOPS [1], KRITIK [2], CADET [3], ARCHIE [4], CADSYN [5], CADRE [6,7], JULIA [8,9], SEED [10], the FABEL project [11][12][13], BOGART [14], and ASA [15] are case-based design (CBD) systems proposed for mechanical engineering, civil engineering and architecture. However, despite the many interesting proposals, many problems need to be solved before applications become useful in practice.…”

Section: Introductionmentioning

confidence: 99%